Overview

Brought to you by YData

Dataset statistics

Number of variables44
Number of observations7691
Missing cells3508
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 MiB
Average record size in memory310.4 B

Variable types

Numeric24
DateTime1
Categorical19

Alerts

TP_type_insd_pass_front has constant value "0" Constant
TP_type_pass_multi has constant value "0" Constant
Claim Number is highly overall correlated with Main_driver and 3 other fieldsHigh correlation
TP_type_driver is highly overall correlated with Claim Number and 1 other fieldsHigh correlation
TP_type_pass_back is highly overall correlated with TP_injury_whiplash and 2 other fieldsHigh correlation
TP_type_pass_front is highly overall correlated with TP_injury_whiplashHigh correlation
TP_type_nk is highly overall correlated with Claim Number and 1 other fieldsHigh correlation
TP_injury_whiplash is highly overall correlated with TP_type_pass_back and 5 other fieldsHigh correlation
Incurred is highly overall correlated with Capped IncurredHigh correlation
Capped Incurred is highly overall correlated with IncurredHigh correlation
Notifier is highly overall correlated with Claim NumberHigh correlation
Vehicle_mobile is highly overall correlated with Time_hourHigh correlation
Time_hour is highly overall correlated with Vehicle_mobileHigh correlation
Main_driver is highly overall correlated with Claim Number and 1 other fieldsHigh correlation
PH_considered_TP_at_fault is highly overall correlated with Claim Number and 1 other fieldsHigh correlation
TP_type_other is highly overall correlated with TP_injury_unclear and 1 other fieldsHigh correlation
TP_injury_traumatic is highly overall correlated with TP_region_northHigh correlation
TP_injury_fatality is highly overall correlated with TP_region_eastangHigh correlation
TP_injury_unclear is highly overall correlated with TP_type_driver and 5 other fieldsHigh correlation
TP_injury_nk is highly overall correlated with TP_type_nk and 1 other fieldsHigh correlation
TP_region_eastang is highly overall correlated with TP_injury_fatalityHigh correlation
TP_region_london is highly overall correlated with TP_type_pass_back and 1 other fieldsHigh correlation
TP_region_north is highly overall correlated with TP_injury_traumaticHigh correlation
TP_region_northw is highly overall correlated with TP_type_other and 1 other fieldsHigh correlation
TP_region_southw is highly overall correlated with TP_injury_unclearHigh correlation
TP_region_westmid is highly overall correlated with TP_type_pass_back and 1 other fieldsHigh correlation
Vechile_registration_present is highly imbalanced (99.1%) Imbalance
TP_type_insd_pass_back is highly imbalanced (92.7%) Imbalance
TP_type_pass_front is highly imbalanced (79.5%) Imbalance
TP_type_bike is highly imbalanced (96.1%) Imbalance
TP_type_cyclist is highly imbalanced (99.4%) Imbalance
TP_type_pedestrian is highly imbalanced (99.8%) Imbalance
TP_injury_traumatic is highly imbalanced (83.7%) Imbalance
TP_injury_fatality is highly imbalanced (97.9%) Imbalance
TP_region_north is highly imbalanced (92.8%) Imbalance
Incurred has 1754 (22.8%) missing values Missing
Capped Incurred has 1754 (22.8%) missing values Missing
Claim Number is uniformly distributed Uniform
Claim Number has unique values Unique
Notification_period has 3433 (44.6%) zeros Zeros
Time_hour has 357 (4.6%) zeros Zeros
TP_type_driver has 2907 (37.8%) zeros Zeros
TP_type_pass_back has 7464 (97.0%) zeros Zeros
TP_type_other has 7257 (94.4%) zeros Zeros
TP_type_nk has 5159 (67.1%) zeros Zeros
TP_injury_whiplash has 6253 (81.3%) zeros Zeros
TP_injury_unclear has 1342 (17.4%) zeros Zeros
TP_injury_nk has 3606 (46.9%) zeros Zeros
TP_region_eastang has 7446 (96.8%) zeros Zeros
TP_region_eastmid has 7382 (96.0%) zeros Zeros
TP_region_london has 7567 (98.4%) zeros Zeros
TP_region_northw has 7321 (95.2%) zeros Zeros
TP_region_outerldn has 7457 (97.0%) zeros Zeros
TP_region_scotland has 7573 (98.5%) zeros Zeros
TP_region_southe has 6969 (90.6%) zeros Zeros
TP_region_southw has 7091 (92.2%) zeros Zeros
TP_region_wales has 7357 (95.7%) zeros Zeros
TP_region_westmid has 7262 (94.4%) zeros Zeros
TP_region_yorkshire has 7231 (94.0%) zeros Zeros

Reproduction

Analysis started2025-07-08 17:22:04.989094
Analysis finished2025-07-08 17:22:28.826788
Duration23.84 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Claim Number
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct7691
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3846
Minimum1
Maximum7691
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:28.865217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile385.5
Q11923.5
median3846
Q35768.5
95-th percentile7306.5
Maximum7691
Range7690
Interquartile range (IQR)3845

Descriptive statistics

Standard deviation2220.3448
Coefficient of variation (CV)0.57731274
Kurtosis-1.2
Mean3846
Median Absolute Deviation (MAD)1923
Skewness0
Sum29579586
Variance4929931
MonotonicityStrictly increasing
2025-07-08T18:22:28.920822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
5288 1
 
< 0.1%
5136 1
 
< 0.1%
5135 1
 
< 0.1%
5134 1
 
< 0.1%
5133 1
 
< 0.1%
5132 1
 
< 0.1%
5131 1
 
< 0.1%
5130 1
 
< 0.1%
5129 1
 
< 0.1%
Other values (7681) 7681
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
7691 1
< 0.1%
7690 1
< 0.1%
7689 1
< 0.1%
7688 1
< 0.1%
7687 1
< 0.1%
7686 1
< 0.1%
7685 1
< 0.1%
7684 1
< 0.1%
7683 1
< 0.1%
7682 1
< 0.1%
Distinct3175
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Memory size60.2 KiB
Minimum2003-04-15 00:00:00
Maximum2015-06-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-08T18:22:28.971006image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:29.114716image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Notifier
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
PH
3807 
Other
3117 
TP
 
325
CNF
 
262
NamedDriver
 
180

Length

Max length11
Median length2
Mean length3.4605383
Min length2

Characters and Unicode

Total characters26615
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPH
2nd rowCNF
3rd rowCNF
4th rowCNF
5th rowCNF

Common Values

ValueCountFrequency (%)
PH 3807
49.5%
Other 3117
40.5%
TP 325
 
4.2%
CNF 262
 
3.4%
NamedDriver 180
 
2.3%

Length

2025-07-08T18:22:29.161238image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T18:22:29.197628image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ph 3807
49.5%
other 3117
40.5%
tp 325
 
4.2%
cnf 262
 
3.4%
nameddriver 180
 
2.3%

Most occurring characters

ValueCountFrequency (%)
P 4132
15.5%
H 3807
14.3%
e 3477
13.1%
r 3477
13.1%
O 3117
11.7%
t 3117
11.7%
h 3117
11.7%
N 442
 
1.7%
T 325
 
1.2%
F 262
 
1.0%
Other values (7) 1342
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 4132
15.5%
H 3807
14.3%
e 3477
13.1%
r 3477
13.1%
O 3117
11.7%
t 3117
11.7%
h 3117
11.7%
N 442
 
1.7%
T 325
 
1.2%
F 262
 
1.0%
Other values (7) 1342
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 4132
15.5%
H 3807
14.3%
e 3477
13.1%
r 3477
13.1%
O 3117
11.7%
t 3117
11.7%
h 3117
11.7%
N 442
 
1.7%
T 325
 
1.2%
F 262
 
1.0%
Other values (7) 1342
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 4132
15.5%
H 3807
14.3%
e 3477
13.1%
r 3477
13.1%
O 3117
11.7%
t 3117
11.7%
h 3117
11.7%
N 442
 
1.7%
T 325
 
1.2%
F 262
 
1.0%
Other values (7) 1342
 
5.0%

Notification_period
Real number (ℝ)

Zeros 

Distinct189
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1634378
Minimum-18
Maximum1042
Zeros3433
Zeros (%)44.6%
Negative3
Negative (%)< 0.1%
Memory size60.2 KiB
2025-07-08T18:22:29.241649image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-18
5-th percentile0
Q10
median1
Q32
95-th percentile24
Maximum1042
Range1060
Interquartile range (IQR)2

Descriptive statistics

Standard deviation39.138209
Coefficient of variation (CV)5.4636071
Kurtosis251.0534
Mean7.1634378
Median Absolute Deviation (MAD)1
Skewness13.596732
Sum55094
Variance1531.7994
MonotonicityNot monotonic
2025-07-08T18:22:29.290637image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3433
44.6%
1 2046
26.6%
2 613
 
8.0%
3 303
 
3.9%
4 183
 
2.4%
5 122
 
1.6%
6 92
 
1.2%
7 75
 
1.0%
8 72
 
0.9%
10 57
 
0.7%
Other values (179) 695
 
9.0%
ValueCountFrequency (%)
-18 1
 
< 0.1%
-2 1
 
< 0.1%
-1 1
 
< 0.1%
0 3433
44.6%
1 2046
26.6%
2 613
 
8.0%
3 303
 
3.9%
4 183
 
2.4%
5 122
 
1.6%
6 92
 
1.2%
ValueCountFrequency (%)
1042 1
< 0.1%
961 1
< 0.1%
925 1
< 0.1%
856 1
< 0.1%
741 1
< 0.1%
720 1
< 0.1%
577 1
< 0.1%
546 1
< 0.1%
519 1
< 0.1%
510 1
< 0.1%

Inception_to_loss
Real number (ℝ)

Distinct366
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.85451
Minimum0
Maximum365
Zeros26
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:29.336101image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q175
median161
Q3253
95-th percentile340
Maximum365
Range365
Interquartile range (IQR)178

Descriptive statistics

Standard deviation104.45291
Coefficient of variation (CV)0.6260119
Kurtosis-1.1557578
Mean166.85451
Median Absolute Deviation (MAD)89
Skewness0.17115136
Sum1283278
Variance10910.41
MonotonicityNot monotonic
2025-07-08T18:22:29.383260image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 42
 
0.5%
91 36
 
0.5%
173 36
 
0.5%
67 36
 
0.5%
51 35
 
0.5%
66 34
 
0.4%
124 33
 
0.4%
102 33
 
0.4%
14 33
 
0.4%
53 33
 
0.4%
Other values (356) 7340
95.4%
ValueCountFrequency (%)
0 26
0.3%
1 32
0.4%
2 22
0.3%
3 27
0.4%
4 28
0.4%
5 27
0.4%
6 22
0.3%
7 30
0.4%
8 32
0.4%
9 24
0.3%
ValueCountFrequency (%)
365 8
 
0.1%
364 22
0.3%
363 17
0.2%
362 19
0.2%
361 12
0.2%
360 21
0.3%
359 9
0.1%
358 17
0.2%
357 20
0.3%
356 19
0.2%
Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
Minor Road
4249 
Main Road
2702 
Car Park
 
225
n/k
 
213
Other
 
117
Other values (3)
 
185

Length

Max length14
Median length10
Mean length9.3699129
Min length3

Characters and Unicode

Total characters72064
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMain Road
2nd rowMain Road
3rd rowMain Road
4th rowMain Road
5th rowOther

Common Values

ValueCountFrequency (%)
Minor Road 4249
55.2%
Main Road 2702
35.1%
Car Park 225
 
2.9%
n/k 213
 
2.8%
Other 117
 
1.5%
Home Address 104
 
1.4%
Not Applicable 56
 
0.7%
Motorway 25
 
0.3%

Length

2025-07-08T18:22:29.429228image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T18:22:29.468389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
road 6951
46.3%
minor 4249
28.3%
main 2702
 
18.0%
car 225
 
1.5%
park 225
 
1.5%
n/k 213
 
1.4%
other 117
 
0.8%
home 104
 
0.7%
address 104
 
0.7%
not 56
 
0.4%
Other values (2) 81
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 11410
15.8%
a 10184
14.1%
7336
10.2%
n 7164
9.9%
d 7159
9.9%
i 7007
9.7%
M 6976
9.7%
R 6951
9.6%
r 4945
6.9%
k 438
 
0.6%
Other values (18) 2494
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72064
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 11410
15.8%
a 10184
14.1%
7336
10.2%
n 7164
9.9%
d 7159
9.9%
i 7007
9.7%
M 6976
9.7%
R 6951
9.6%
r 4945
6.9%
k 438
 
0.6%
Other values (18) 2494
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72064
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 11410
15.8%
a 10184
14.1%
7336
10.2%
n 7164
9.9%
d 7159
9.9%
i 7007
9.7%
M 6976
9.7%
R 6951
9.6%
r 4945
6.9%
k 438
 
0.6%
Other values (18) 2494
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72064
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 11410
15.8%
a 10184
14.1%
7336
10.2%
n 7164
9.9%
d 7159
9.9%
i 7007
9.7%
M 6976
9.7%
R 6951
9.6%
r 4945
6.9%
k 438
 
0.6%
Other values (18) 2494
 
3.5%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
NORMAL
4564 
WET
1903 
N/K
795 
SNOW,ICE,FOG
 
429

Length

Max length12
Median length6
Mean length5.282278
Min length3

Characters and Unicode

Total characters40626
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORMAL
2nd rowWET
3rd rowWET
4th rowN/K
5th rowN/K

Common Values

ValueCountFrequency (%)
NORMAL 4564
59.3%
WET 1903
24.7%
N/K 795
 
10.3%
SNOW,ICE,FOG 429
 
5.6%

Length

2025-07-08T18:22:29.512819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T18:22:29.546912image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
normal 4564
59.3%
wet 1903
24.7%
n/k 795
 
10.3%
snow,ice,fog 429
 
5.6%

Most occurring characters

ValueCountFrequency (%)
N 5788
14.2%
O 5422
13.3%
R 4564
11.2%
M 4564
11.2%
A 4564
11.2%
L 4564
11.2%
W 2332
5.7%
E 2332
5.7%
T 1903
 
4.7%
, 858
 
2.1%
Other values (7) 3735
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 5788
14.2%
O 5422
13.3%
R 4564
11.2%
M 4564
11.2%
A 4564
11.2%
L 4564
11.2%
W 2332
5.7%
E 2332
5.7%
T 1903
 
4.7%
, 858
 
2.1%
Other values (7) 3735
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 5788
14.2%
O 5422
13.3%
R 4564
11.2%
M 4564
11.2%
A 4564
11.2%
L 4564
11.2%
W 2332
5.7%
E 2332
5.7%
T 1903
 
4.7%
, 858
 
2.1%
Other values (7) 3735
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 5788
14.2%
O 5422
13.3%
R 4564
11.2%
M 4564
11.2%
A 4564
11.2%
L 4564
11.2%
W 2332
5.7%
E 2332
5.7%
T 1903
 
4.7%
, 858
 
2.1%
Other values (7) 3735
9.2%

Vehicle_mobile
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Y
4046 
N
3203 
n/k
442 

Length

Max length3
Median length1
Mean length1.1149395
Min length1

Characters and Unicode

Total characters8575
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowN

Common Values

ValueCountFrequency (%)
Y 4046
52.6%
N 3203
41.6%
n/k 442
 
5.7%

Length

2025-07-08T18:22:29.592291image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T18:22:29.630383image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
y 4046
52.6%
n 3203
41.6%
n/k 442
 
5.7%

Most occurring characters

ValueCountFrequency (%)
Y 4046
47.2%
N 3203
37.4%
n 442
 
5.2%
/ 442
 
5.2%
k 442
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8575
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 4046
47.2%
N 3203
37.4%
n 442
 
5.2%
/ 442
 
5.2%
k 442
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8575
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 4046
47.2%
N 3203
37.4%
n 442
 
5.2%
/ 442
 
5.2%
k 442
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8575
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 4046
47.2%
N 3203
37.4%
n 442
 
5.2%
/ 442
 
5.2%
k 442
 
5.2%

Time_hour
Real number (ℝ)

High correlation  Zeros 

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.729684
Minimum0
Maximum23
Zeros357
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:29.665656image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q19
median13
Q317
95-th percentile20
Maximum23
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.1071358
Coefficient of variation (CV)0.40119895
Kurtosis-0.051858402
Mean12.729684
Median Absolute Deviation (MAD)4
Skewness-0.4975176
Sum97904
Variance26.082837
MonotonicityNot monotonic
2025-07-08T18:22:29.704945image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
8 747
 
9.7%
17 682
 
8.9%
15 612
 
8.0%
16 582
 
7.6%
18 556
 
7.2%
14 483
 
6.3%
13 481
 
6.3%
9 467
 
6.1%
12 449
 
5.8%
11 422
 
5.5%
Other values (14) 2210
28.7%
ValueCountFrequency (%)
0 357
4.6%
1 19
 
0.2%
2 14
 
0.2%
3 15
 
0.2%
4 12
 
0.2%
5 52
 
0.7%
6 121
 
1.6%
7 417
5.4%
8 747
9.7%
9 467
6.1%
ValueCountFrequency (%)
23 67
 
0.9%
22 109
 
1.4%
21 116
 
1.5%
20 205
 
2.7%
19 334
4.3%
18 556
7.2%
17 682
8.9%
16 582
7.6%
15 612
8.0%
14 483
6.3%

Main_driver
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Y
4319 
Other
2940 
N
432 

Length

Max length5
Median length1
Mean length2.5290599
Min length1

Characters and Unicode

Total characters19451
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowOther
3rd rowY
4th rowOther
5th rowOther

Common Values

ValueCountFrequency (%)
Y 4319
56.2%
Other 2940
38.2%
N 432
 
5.6%

Length

2025-07-08T18:22:29.748535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T18:22:29.785389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
y 4319
56.2%
other 2940
38.2%
n 432
 
5.6%

Most occurring characters

ValueCountFrequency (%)
Y 4319
22.2%
O 2940
15.1%
t 2940
15.1%
h 2940
15.1%
e 2940
15.1%
r 2940
15.1%
N 432
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 4319
22.2%
O 2940
15.1%
t 2940
15.1%
h 2940
15.1%
e 2940
15.1%
r 2940
15.1%
N 432
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 4319
22.2%
O 2940
15.1%
t 2940
15.1%
h 2940
15.1%
e 2940
15.1%
r 2940
15.1%
N 432
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 4319
22.2%
O 2940
15.1%
t 2940
15.1%
h 2940
15.1%
e 2940
15.1%
r 2940
15.1%
N 432
 
2.2%

PH_considered_TP_at_fault
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.0 KiB
N
4855 
n/k
2654 
Y
 
181
#
 
1

Length

Max length3
Median length1
Mean length1.6901573
Min length1

Characters and Unicode

Total characters12999
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rown/k
2nd rown/k
3rd rown/k
4th rown/k
5th rown/k

Common Values

ValueCountFrequency (%)
N 4855
63.1%
n/k 2654
34.5%
Y 181
 
2.4%
# 1
 
< 0.1%

Length

2025-07-08T18:22:29.826957image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T18:22:29.865871image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
n 4855
63.1%
n/k 2654
34.5%
y 181
 
2.4%
1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 4855
37.3%
n 2654
20.4%
/ 2654
20.4%
k 2654
20.4%
Y 181
 
1.4%
# 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12999
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 4855
37.3%
n 2654
20.4%
/ 2654
20.4%
k 2654
20.4%
Y 181
 
1.4%
# 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12999
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 4855
37.3%
n 2654
20.4%
/ 2654
20.4%
k 2654
20.4%
Y 181
 
1.4%
# 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12999
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 4855
37.3%
n 2654
20.4%
/ 2654
20.4%
k 2654
20.4%
Y 181
 
1.4%
# 1
 
< 0.1%

Vechile_registration_present
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size435.8 KiB
1
7685 
0
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7691
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 7685
99.9%
0 6
 
0.1%

Length

2025-07-08T18:22:29.903371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T18:22:29.935293image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 7685
99.9%
0 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 7685
99.9%
0 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 7685
99.9%
0 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 7685
99.9%
0 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 7685
99.9%
0 6
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size435.8 KiB
1
6217 
0
1474 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7691
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6217
80.8%
0 1474
 
19.2%

Length

2025-07-08T18:22:29.968456image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T18:22:30.001448image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 6217
80.8%
0 1474
 
19.2%

Most occurring characters

ValueCountFrequency (%)
1 6217
80.8%
0 1474
 
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6217
80.8%
0 1474
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6217
80.8%
0 1474
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6217
80.8%
0 1474
 
19.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size435.8 KiB
0
5906 
1
1785 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7691
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5906
76.8%
1 1785
 
23.2%

Length

2025-07-08T18:22:30.035645image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T18:22:30.068398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 5906
76.8%
1 1785
 
23.2%

Most occurring characters

ValueCountFrequency (%)
0 5906
76.8%
1 1785
 
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5906
76.8%
1 1785
 
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5906
76.8%
1 1785
 
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5906
76.8%
1 1785
 
23.2%

TP_type_insd_pass_back
Categorical

Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size435.8 KiB
0
7531 
1
 
110
2
 
38
3
 
11
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7691
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7531
97.9%
1 110
 
1.4%
2 38
 
0.5%
3 11
 
0.1%
4 1
 
< 0.1%

Length

2025-07-08T18:22:30.102721image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T18:22:30.137108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 7531
97.9%
1 110
 
1.4%
2 38
 
0.5%
3 11
 
0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 7531
97.9%
1 110
 
1.4%
2 38
 
0.5%
3 11
 
0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7531
97.9%
1 110
 
1.4%
2 38
 
0.5%
3 11
 
0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7531
97.9%
1 110
 
1.4%
2 38
 
0.5%
3 11
 
0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7531
97.9%
1 110
 
1.4%
2 38
 
0.5%
3 11
 
0.1%
4 1
 
< 0.1%

TP_type_insd_pass_front
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size435.8 KiB
0
7691 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7691
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7691
100.0%

Length

2025-07-08T18:22:30.174629image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T18:22:30.205461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 7691
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7691
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7691
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7691
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7691
100.0%

TP_type_driver
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65167078
Minimum0
Maximum5
Zeros2907
Zeros (%)37.8%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:30.234787image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.54472599
Coefficient of variation (CV)0.83589138
Kurtosis1.1214256
Mean0.65167078
Median Absolute Deviation (MAD)0
Skewness0.26599014
Sum5012
Variance0.2967264
MonotonicityNot monotonic
2025-07-08T18:22:30.268797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 4588
59.7%
0 2907
37.8%
2 170
 
2.2%
3 22
 
0.3%
4 2
 
< 0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
0 2907
37.8%
1 4588
59.7%
2 170
 
2.2%
3 22
 
0.3%
4 2
 
< 0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
4 2
 
< 0.1%
3 22
 
0.3%
2 170
 
2.2%
1 4588
59.7%
0 2907
37.8%

TP_type_pass_back
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.041737095
Minimum0
Maximum6
Zeros7464
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:30.300631image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.27358868
Coefficient of variation (CV)6.5550484
Kurtosis106.26934
Mean0.041737095
Median Absolute Deviation (MAD)0
Skewness8.9328798
Sum321
Variance0.074850766
MonotonicityNot monotonic
2025-07-08T18:22:30.336162image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 7464
97.0%
1 160
 
2.1%
2 48
 
0.6%
3 15
 
0.2%
5 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 7464
97.0%
1 160
 
2.1%
2 48
 
0.6%
3 15
 
0.2%
4 1
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 2
 
< 0.1%
4 1
 
< 0.1%
3 15
 
0.2%
2 48
 
0.6%
1 160
 
2.1%
0 7464
97.0%

TP_type_pass_front
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size435.8 KiB
0
7264 
1
 
405
2
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7691
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7264
94.4%
1 405
 
5.3%
2 22
 
0.3%

Length

2025-07-08T18:22:30.374719image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T18:22:30.409784image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 7264
94.4%
1 405
 
5.3%
2 22
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 7264
94.4%
1 405
 
5.3%
2 22
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7264
94.4%
1 405
 
5.3%
2 22
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7264
94.4%
1 405
 
5.3%
2 22
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7264
94.4%
1 405
 
5.3%
2 22
 
0.3%

TP_type_bike
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size435.8 KiB
0
7637 
1
 
52
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7691
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7637
99.3%
1 52
 
0.7%
2 2
 
< 0.1%

Length

2025-07-08T18:22:30.446868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T18:22:30.480645image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 7637
99.3%
1 52
 
0.7%
2 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 7637
99.3%
1 52
 
0.7%
2 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7637
99.3%
1 52
 
0.7%
2 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7637
99.3%
1 52
 
0.7%
2 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7637
99.3%
1 52
 
0.7%
2 2
 
< 0.1%

TP_type_cyclist
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size435.8 KiB
0
7687 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7691
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7687
99.9%
1 4
 
0.1%

Length

2025-07-08T18:22:30.517702image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T18:22:30.550482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 7687
99.9%
1 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 7687
99.9%
1 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7687
99.9%
1 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7687
99.9%
1 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7687
99.9%
1 4
 
0.1%

TP_type_pass_multi
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size435.8 KiB
0
7691 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7691
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7691
100.0%

Length

2025-07-08T18:22:30.584850image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T18:22:30.615771image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 7691
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7691
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7691
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7691
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7691
100.0%

TP_type_pedestrian
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size435.8 KiB
0
7690 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7691
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7690
> 99.9%
1 1
 
< 0.1%

Length

2025-07-08T18:22:30.647787image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T18:22:30.680413image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 7690
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 7690
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7690
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7690
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7690
> 99.9%
1 1
 
< 0.1%

TP_type_other
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.075022754
Minimum0
Maximum6
Zeros7257
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:30.709734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.35620304
Coefficient of variation (CV)4.7479334
Kurtosis60.696465
Mean0.075022754
Median Absolute Deviation (MAD)0
Skewness6.7349992
Sum577
Variance0.12688061
MonotonicityNot monotonic
2025-07-08T18:22:30.745547image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 7257
94.4%
1 345
 
4.5%
2 52
 
0.7%
3 26
 
0.3%
4 6
 
0.1%
5 4
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 7257
94.4%
1 345
 
4.5%
2 52
 
0.7%
3 26
 
0.3%
4 6
 
0.1%
5 4
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 4
 
0.1%
4 6
 
0.1%
3 26
 
0.3%
2 52
 
0.7%
1 345
 
4.5%
0 7257
94.4%

TP_type_nk
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34936939
Minimum0
Maximum6
Zeros5159
Zeros (%)67.1%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:30.779548image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.52334878
Coefficient of variation (CV)1.4979812
Kurtosis2.5048598
Mean0.34936939
Median Absolute Deviation (MAD)0
Skewness1.3025749
Sum2687
Variance0.27389395
MonotonicityNot monotonic
2025-07-08T18:22:30.812804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 5159
67.1%
1 2394
31.1%
2 125
 
1.6%
3 11
 
0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 5159
67.1%
1 2394
31.1%
2 125
 
1.6%
3 11
 
0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
4 1
 
< 0.1%
3 11
 
0.1%
2 125
 
1.6%
1 2394
31.1%
0 5159
67.1%

TP_injury_whiplash
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26251463
Minimum0
Maximum7
Zeros6253
Zeros (%)81.3%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:30.846834image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.64295552
Coefficient of variation (CV)2.4492179
Kurtosis14.916662
Mean0.26251463
Median Absolute Deviation (MAD)0
Skewness3.315176
Sum2019
Variance0.4133918
MonotonicityNot monotonic
2025-07-08T18:22:30.882312image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 6253
81.3%
1 1035
 
13.5%
2 283
 
3.7%
3 82
 
1.1%
4 24
 
0.3%
5 10
 
0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
0 6253
81.3%
1 1035
 
13.5%
2 283
 
3.7%
3 82
 
1.1%
4 24
 
0.3%
5 10
 
0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
7 2
 
< 0.1%
6 2
 
< 0.1%
5 10
 
0.1%
4 24
 
0.3%
3 82
 
1.1%
2 283
 
3.7%
1 1035
 
13.5%
0 6253
81.3%

TP_injury_traumatic
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size435.8 KiB
0
7214 
1
 
404
2
 
65
3
 
4
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7691
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7214
93.8%
1 404
 
5.3%
2 65
 
0.8%
3 4
 
0.1%
4 4
 
0.1%

Length

2025-07-08T18:22:30.922167image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T18:22:30.956681image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 7214
93.8%
1 404
 
5.3%
2 65
 
0.8%
3 4
 
0.1%
4 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 7214
93.8%
1 404
 
5.3%
2 65
 
0.8%
3 4
 
0.1%
4 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7214
93.8%
1 404
 
5.3%
2 65
 
0.8%
3 4
 
0.1%
4 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7214
93.8%
1 404
 
5.3%
2 65
 
0.8%
3 4
 
0.1%
4 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7214
93.8%
1 404
 
5.3%
2 65
 
0.8%
3 4
 
0.1%
4 4
 
0.1%

TP_injury_fatality
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size435.8 KiB
0
7665 
1
 
24
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7691
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7665
99.7%
1 24
 
0.3%
2 2
 
< 0.1%

Length

2025-07-08T18:22:30.993582image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T18:22:31.027041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 7665
99.7%
1 24
 
0.3%
2 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 7665
99.7%
1 24
 
0.3%
2 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7665
99.7%
1 24
 
0.3%
2 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7665
99.7%
1 24
 
0.3%
2 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7665
99.7%
1 24
 
0.3%
2 2
 
< 0.1%

TP_injury_unclear
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.91613574
Minimum0
Maximum7
Zeros1342
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:31.057255image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.55945677
Coefficient of variation (CV)0.61067017
Kurtosis10.561416
Mean0.91613574
Median Absolute Deviation (MAD)0
Skewness1.3601861
Sum7046
Variance0.31299188
MonotonicityNot monotonic
2025-07-08T18:22:31.093882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 5797
75.4%
0 1342
 
17.4%
2 449
 
5.8%
3 77
 
1.0%
4 16
 
0.2%
5 6
 
0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
0 1342
 
17.4%
1 5797
75.4%
2 449
 
5.8%
3 77
 
1.0%
4 16
 
0.2%
5 6
 
0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
7 2
 
< 0.1%
6 2
 
< 0.1%
5 6
 
0.1%
4 16
 
0.2%
3 77
 
1.0%
2 449
 
5.8%
1 5797
75.4%
0 1342
 
17.4%

TP_injury_nk
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5958913
Minimum0
Maximum6
Zeros3606
Zeros (%)46.9%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:31.128536image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6466911
Coefficient of variation (CV)1.0852501
Kurtosis4.8719814
Mean0.5958913
Median Absolute Deviation (MAD)1
Skewness1.3487761
Sum4583
Variance0.41820938
MonotonicityNot monotonic
2025-07-08T18:22:31.163227image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 3716
48.3%
0 3606
46.9%
2 284
 
3.7%
3 51
 
0.7%
4 25
 
0.3%
5 8
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 3606
46.9%
1 3716
48.3%
2 284
 
3.7%
3 51
 
0.7%
4 25
 
0.3%
5 8
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 8
 
0.1%
4 25
 
0.3%
3 51
 
0.7%
2 284
 
3.7%
1 3716
48.3%
0 3606
46.9%

TP_region_eastang
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.039396697
Minimum0
Maximum5
Zeros7446
Zeros (%)96.8%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:31.199300image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.24165199
Coefficient of variation (CV)6.1338133
Kurtosis86.538962
Mean0.039396697
Median Absolute Deviation (MAD)0
Skewness8.1064198
Sum303
Variance0.058395683
MonotonicityNot monotonic
2025-07-08T18:22:31.234766image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 7446
96.8%
1 204
 
2.7%
2 27
 
0.4%
3 12
 
0.2%
4 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 7446
96.8%
1 204
 
2.7%
2 27
 
0.4%
3 12
 
0.2%
4 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 1
 
< 0.1%
3 12
 
0.2%
2 27
 
0.4%
1 204
 
2.7%
0 7446
96.8%

TP_region_eastmid
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.050968665
Minimum0
Maximum6
Zeros7382
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:31.268429image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2862293
Coefficient of variation (CV)5.6157898
Kurtosis102.10601
Mean0.050968665
Median Absolute Deviation (MAD)0
Skewness8.3571089
Sum392
Variance0.081927215
MonotonicityNot monotonic
2025-07-08T18:22:31.303439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 7382
96.0%
1 254
 
3.3%
2 38
 
0.5%
3 11
 
0.1%
4 3
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 7382
96.0%
1 254
 
3.3%
2 38
 
0.5%
3 11
 
0.1%
4 3
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
6 2
 
< 0.1%
5 1
 
< 0.1%
4 3
 
< 0.1%
3 11
 
0.1%
2 38
 
0.5%
1 254
 
3.3%
0 7382
96.0%

TP_region_london
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.021583669
Minimum0
Maximum9
Zeros7567
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:31.338636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.21528616
Coefficient of variation (CV)9.9744929
Kurtosis577.26637
Mean0.021583669
Median Absolute Deviation (MAD)0
Skewness19.460447
Sum166
Variance0.046348129
MonotonicityNot monotonic
2025-07-08T18:22:31.371608image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 7567
98.4%
1 104
 
1.4%
2 12
 
0.2%
4 3
 
< 0.1%
3 2
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 7567
98.4%
1 104
 
1.4%
2 12
 
0.2%
3 2
 
< 0.1%
4 3
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
4 3
 
< 0.1%
3 2
 
< 0.1%
2 12
 
0.2%
1 104
 
1.4%
0 7567
98.4%

TP_region_north
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size435.8 KiB
0
7528 
1
 
130
2
 
23
3
 
9
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7691
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7528
97.9%
1 130
 
1.7%
2 23
 
0.3%
3 9
 
0.1%
4 1
 
< 0.1%

Length

2025-07-08T18:22:31.407014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T18:22:31.442059image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 7528
97.9%
1 130
 
1.7%
2 23
 
0.3%
3 9
 
0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 7528
97.9%
1 130
 
1.7%
2 23
 
0.3%
3 9
 
0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7528
97.9%
1 130
 
1.7%
2 23
 
0.3%
3 9
 
0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7528
97.9%
1 130
 
1.7%
2 23
 
0.3%
3 9
 
0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7528
97.9%
1 130
 
1.7%
2 23
 
0.3%
3 9
 
0.1%
4 1
 
< 0.1%

TP_region_northw
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.066831361
Minimum0
Maximum7
Zeros7321
Zeros (%)95.2%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:31.474681image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.34844046
Coefficient of variation (CV)5.2137268
Kurtosis82.915122
Mean0.066831361
Median Absolute Deviation (MAD)0
Skewness7.6759963
Sum514
Variance0.12141075
MonotonicityNot monotonic
2025-07-08T18:22:31.511885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 7321
95.2%
1 276
 
3.6%
2 64
 
0.8%
3 18
 
0.2%
4 8
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 7321
95.2%
1 276
 
3.6%
2 64
 
0.8%
3 18
 
0.2%
4 8
 
0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%
4 8
 
0.1%
3 18
 
0.2%
2 64
 
0.8%
1 276
 
3.6%
0 7321
95.2%

TP_region_outerldn
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.036406189
Minimum0
Maximum5
Zeros7457
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:31.547674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.22801406
Coefficient of variation (CV)6.2630576
Kurtosis100.11968
Mean0.036406189
Median Absolute Deviation (MAD)0
Skewness8.514174
Sum280
Variance0.051990412
MonotonicityNot monotonic
2025-07-08T18:22:31.583018image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 7457
97.0%
1 202
 
2.6%
2 22
 
0.3%
3 7
 
0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 7457
97.0%
1 202
 
2.6%
2 22
 
0.3%
3 7
 
0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 2
 
< 0.1%
3 7
 
0.1%
2 22
 
0.3%
1 202
 
2.6%
0 7457
97.0%

TP_region_scotland
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.019893382
Minimum0
Maximum6
Zeros7573
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:31.616814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.18736289
Coefficient of variation (CV)9.4183528
Kurtosis328.57431
Mean0.019893382
Median Absolute Deviation (MAD)0
Skewness15.046054
Sum153
Variance0.035104852
MonotonicityNot monotonic
2025-07-08T18:22:31.651130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 7573
98.5%
1 95
 
1.2%
2 19
 
0.2%
5 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 7573
98.5%
1 95
 
1.2%
2 19
 
0.2%
4 1
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 2
 
< 0.1%
4 1
 
< 0.1%
2 19
 
0.2%
1 95
 
1.2%
0 7573
98.5%

TP_region_southe
Real number (ℝ)

Zeros 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11545963
Minimum0
Maximum9
Zeros6969
Zeros (%)90.6%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:31.687217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.41952848
Coefficient of variation (CV)3.6335512
Kurtosis65.374325
Mean0.11545963
Median Absolute Deviation (MAD)0
Skewness6.1291487
Sum888
Variance0.17600414
MonotonicityNot monotonic
2025-07-08T18:22:31.811100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 6969
90.6%
1 618
 
8.0%
2 69
 
0.9%
3 23
 
0.3%
5 5
 
0.1%
4 4
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 6969
90.6%
1 618
 
8.0%
2 69
 
0.9%
3 23
 
0.3%
4 4
 
0.1%
5 5
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
5 5
 
0.1%
4 4
 
0.1%
3 23
 
0.3%
2 69
 
0.9%
1 618
 
8.0%
0 6969
90.6%

TP_region_southw
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09023534
Minimum0
Maximum5
Zeros7091
Zeros (%)92.2%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:31.844446image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.33892983
Coefficient of variation (CV)3.7560653
Kurtosis32.567112
Mean0.09023534
Median Absolute Deviation (MAD)0
Skewness4.8482904
Sum694
Variance0.11487343
MonotonicityNot monotonic
2025-07-08T18:22:31.880323image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 7091
92.2%
1 530
 
6.9%
2 53
 
0.7%
3 11
 
0.1%
4 5
 
0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 7091
92.2%
1 530
 
6.9%
2 53
 
0.7%
3 11
 
0.1%
4 5
 
0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 5
 
0.1%
3 11
 
0.1%
2 53
 
0.7%
1 530
 
6.9%
0 7091
92.2%

TP_region_wales
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.056559615
Minimum0
Maximum7
Zeros7357
Zeros (%)95.7%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:31.913278image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.31196274
Coefficient of variation (CV)5.5156446
Kurtosis102.85296
Mean0.056559615
Median Absolute Deviation (MAD)0
Skewness8.4598424
Sum435
Variance0.09732075
MonotonicityNot monotonic
2025-07-08T18:22:31.949614image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 7357
95.7%
1 274
 
3.6%
2 36
 
0.5%
3 14
 
0.2%
4 5
 
0.1%
5 4
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 7357
95.7%
1 274
 
3.6%
2 36
 
0.5%
3 14
 
0.2%
4 5
 
0.1%
5 4
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
5 4
 
0.1%
4 5
 
0.1%
3 14
 
0.2%
2 36
 
0.5%
1 274
 
3.6%
0 7357
95.7%

TP_region_westmid
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.073982577
Minimum0
Maximum6
Zeros7262
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:31.983998image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.35312167
Coefficient of variation (CV)4.7730383
Kurtosis61.205939
Mean0.073982577
Median Absolute Deviation (MAD)0
Skewness6.7678351
Sum569
Variance0.12469492
MonotonicityNot monotonic
2025-07-08T18:22:32.019659image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 7262
94.4%
1 341
 
4.4%
2 54
 
0.7%
3 21
 
0.3%
4 9
 
0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 7262
94.4%
1 341
 
4.4%
2 54
 
0.7%
3 21
 
0.3%
4 9
 
0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 3
 
< 0.1%
4 9
 
0.1%
3 21
 
0.3%
2 54
 
0.7%
1 341
 
4.4%
0 7262
94.4%

TP_region_yorkshire
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.079443505
Minimum0
Maximum5
Zeros7231
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:32.054000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.36356493
Coefficient of variation (CV)4.5763958
Kurtosis52.544739
Mean0.079443505
Median Absolute Deviation (MAD)0
Skewness6.3469228
Sum611
Variance0.13217946
MonotonicityNot monotonic
2025-07-08T18:22:32.090357image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 7231
94.0%
1 362
 
4.7%
2 64
 
0.8%
3 19
 
0.2%
4 11
 
0.1%
5 4
 
0.1%
ValueCountFrequency (%)
0 7231
94.0%
1 362
 
4.7%
2 64
 
0.8%
3 19
 
0.2%
4 11
 
0.1%
5 4
 
0.1%
ValueCountFrequency (%)
5 4
 
0.1%
4 11
 
0.1%
3 19
 
0.2%
2 64
 
0.8%
1 362
 
4.7%
0 7231
94.0%

Incurred
Real number (ℝ)

High correlation  Missing 

Distinct4295
Distinct (%)72.3%
Missing1754
Missing (%)22.8%
Infinite0
Infinite (%)0.0%
Mean12219.117
Minimum0
Maximum1951894
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:32.133640image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile74
Q1640
median2598
Q39767
95-th percentile38025.2
Maximum1951894
Range1951894
Interquartile range (IQR)9127

Descriptive statistics

Standard deviation56438.896
Coefficient of variation (CV)4.6189012
Kurtosis496.30641
Mean12219.117
Median Absolute Deviation (MAD)2413
Skewness18.915814
Sum72544900
Variance3.185349 × 109
MonotonicityNot monotonic
2025-07-08T18:22:32.185359image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 55
 
0.7%
24 28
 
0.4%
86 23
 
0.3%
19 18
 
0.2%
78 18
 
0.2%
85 18
 
0.2%
84 18
 
0.2%
70 16
 
0.2%
142 15
 
0.2%
72 14
 
0.2%
Other values (4285) 5714
74.3%
(Missing) 1754
 
22.8%
ValueCountFrequency (%)
0 4
0.1%
1 5
0.1%
3 1
 
< 0.1%
5 2
 
< 0.1%
7 3
< 0.1%
8 4
0.1%
10 1
 
< 0.1%
13 1
 
< 0.1%
15 1
 
< 0.1%
18 1
 
< 0.1%
ValueCountFrequency (%)
1951894 1
< 0.1%
1810331 1
< 0.1%
1157014 1
< 0.1%
890836 1
< 0.1%
840470 1
< 0.1%
838172 1
< 0.1%
793205 1
< 0.1%
749989 1
< 0.1%
723725 1
< 0.1%
717589 1
< 0.1%

Capped Incurred
Real number (ℝ)

High correlation  Missing 

Distinct4098
Distinct (%)69.0%
Missing1754
Missing (%)22.8%
Infinite0
Infinite (%)0.0%
Mean7923.0109
Minimum0
Maximum50000
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-08T18:22:32.235664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile74
Q1640
median2598
Q39767
95-th percentile38025.2
Maximum50000
Range50000
Interquartile range (IQR)9127

Descriptive statistics

Standard deviation11996.939
Coefficient of variation (CV)1.5141893
Kurtosis4.2142425
Mean7923.0109
Median Absolute Deviation (MAD)2413
Skewness2.1762661
Sum47038916
Variance1.4392653 × 108
MonotonicityNot monotonic
2025-07-08T18:22:32.283647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 198
 
2.6%
23 55
 
0.7%
24 28
 
0.4%
86 23
 
0.3%
85 18
 
0.2%
78 18
 
0.2%
84 18
 
0.2%
19 18
 
0.2%
70 16
 
0.2%
142 15
 
0.2%
Other values (4088) 5530
71.9%
(Missing) 1754
 
22.8%
ValueCountFrequency (%)
0 4
0.1%
1 5
0.1%
3 1
 
< 0.1%
5 2
 
< 0.1%
7 3
< 0.1%
8 4
0.1%
10 1
 
< 0.1%
13 1
 
< 0.1%
15 1
 
< 0.1%
18 1
 
< 0.1%
ValueCountFrequency (%)
50000 198
2.6%
49909 1
 
< 0.1%
49604 1
 
< 0.1%
49391 1
 
< 0.1%
49208 1
 
< 0.1%
49149 1
 
< 0.1%
49137 1
 
< 0.1%
49062 1
 
< 0.1%
48880 1
 
< 0.1%
48867 1
 
< 0.1%

Interactions

2025-07-08T18:22:27.621923image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:07.189880image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:08.305149image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:09.143059image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:10.044992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:10.951419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:11.761487image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:12.594723image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:13.522808image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:14.346331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:15.242892image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:16.195234image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:17.062100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:17.866487image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:18.805687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:19.597673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:20.426369image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:21.368401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:22.348709image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:23.169871image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:23.998996image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:24.943658image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:25.779356image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:26.650009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:27.659024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:07.267635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:08.341837image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:09.182856image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:10.081258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:10.987392image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:11.799854image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:12.631430image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:13.559307image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:14.384215image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:15.281802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:16.233403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:17.102109image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-07-08T18:22:11.557344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:12.384521image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:13.222991image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:14.137901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:15.020105image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:15.885727image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:16.861569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:17.666862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:18.513557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:19.396899image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:20.207752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:21.100224image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:22.131713image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:22.960038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:23.780401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:24.730123image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:25.569070image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:26.444219image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:27.397930image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:28.287254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:08.009389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:08.965268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:09.862489image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:10.674453image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:11.589730image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:12.418593image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:13.255813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:14.172004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:15.056900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:15.921579image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:16.892829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:17.698200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:18.545729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:19.430498image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:20.246319image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:21.133163image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:22.166399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:22.998503image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:23.816610image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:24.764063image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:25.602001image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:26.476631image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:27.437023image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:28.324604image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:08.047537image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:09.003256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:09.900715image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:10.716788image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:11.625087image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:12.453458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:13.292146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:14.207362image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:15.094478image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:15.959090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:16.926654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:17.732795image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:18.579806image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:19.464514image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:20.283490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:21.221732image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:22.205039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:23.034850image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:23.852675image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:24.801008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:25.637300image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:26.511817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:27.474904image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:28.362964image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:08.192780image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:09.037453image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:09.936239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:10.752863image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:11.658968image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:12.489455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:13.326201image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:14.242361image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:15.132743image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:16.087526image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:16.960645image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:17.764998image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:18.613838image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:19.498749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:20.318535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:21.258718image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:22.240864image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:23.067884image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:23.887495image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:24.837235image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:25.672465image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:26.544923image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:27.510907image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:28.396377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:08.229129image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:09.071374image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:09.970538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:10.788391image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:11.691207image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:12.522960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:13.359808image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:14.275112image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:15.167591image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:16.122781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:16.992316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:17.797079image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:18.646054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:19.529455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:20.353051image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:21.294653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:22.274505image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:23.101428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:23.923455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:24.871259image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:25.704791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:26.579134image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:27.547115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:28.434183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:08.269458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:09.109944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:10.010057image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:10.918156image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:11.729200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:12.561546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:13.397990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:14.312620image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:15.208206image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:16.161877image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:17.029106image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:17.833287image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:18.772518image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:19.565662image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:20.391735image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:21.334688image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:22.314564image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:23.137561image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:23.963328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:24.909512image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:25.745558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:26.616402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T18:22:27.585611image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-07-08T18:22:32.333186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Claim NumberNotification_periodInception_to_lossTime_hourVechile_registration_presentIncident_details_presentInjury_details_presentTP_type_insd_pass_backTP_type_insd_pass_frontTP_type_driverTP_type_pass_backTP_type_pass_frontTP_type_bikeTP_type_cyclistTP_type_pass_multiTP_type_pedestrianTP_type_otherTP_type_nkTP_injury_whiplashTP_injury_traumaticTP_injury_fatalityTP_injury_unclearTP_injury_nkTP_region_eastangTP_region_eastmidTP_region_londonTP_region_northTP_region_northwTP_region_outerldnTP_region_scotlandTP_region_southeTP_region_southwTP_region_walesTP_region_westmidTP_region_yorkshireIncurredCapped Incurred
Claim Number1.0000.0260.028-0.0010.0110.222-0.0220.019NaN0.6010.0300.0270.0140.015NaN0.019-0.082-0.6280.0410.0770.002-0.0860.155-0.032-0.0210.004-0.010-0.015-0.0110.005-0.070-0.084-0.019-0.003-0.023-0.017-0.002
Notification_period0.0261.000-0.021-0.156-0.002-0.050-0.068-0.006NaN-0.0120.003-0.0000.0150.017NaN-0.002-0.010-0.0350.011-0.0090.019-0.062-0.001-0.0110.0060.028-0.004-0.019-0.016-0.007-0.005-0.024-0.010-0.011-0.0140.004-0.014
Inception_to_loss0.028-0.0211.000-0.0070.0120.014-0.012-0.031NaN0.012-0.0040.006-0.0100.021NaN0.000-0.008-0.014-0.002-0.004-0.017-0.010-0.0070.026-0.002-0.003-0.005-0.008-0.002-0.015-0.0110.002-0.0160.023-0.0120.008-0.002
Time_hour-0.001-0.156-0.0071.000-0.0110.0240.1040.059NaN0.0160.0040.0340.019-0.001NaN0.005-0.006-0.0110.0490.0220.0060.0120.0020.0180.0110.0080.0250.021-0.0020.0010.0050.0210.0140.0130.0120.0080.026
Vechile_registration_present0.011-0.0020.012-0.0111.000-0.0140.0040.004NaN-0.001-0.0130.0070.0020.001NaN0.000-0.046-0.008-0.0180.0070.002-0.071-0.0170.0050.0050.0030.0040.0050.0040.003-0.026-0.006-0.1140.0060.0060.0050.014
Incident_details_present0.222-0.0500.0140.024-0.0141.0000.0980.019NaN0.2240.0190.0130.003-0.003NaN0.006-0.000-0.2380.0320.015-0.008-0.0160.0520.000-0.009-0.002-0.0040.015-0.0000.004-0.026-0.010-0.0030.012-0.023-0.0010.010
Injury_details_present-0.022-0.068-0.0120.1040.0040.0981.0000.135NaN0.0730.0220.089-0.0030.001NaN0.0210.057-0.0210.1130.1770.0310.0350.0680.0290.0560.0050.0460.0330.0300.0630.0280.0020.0350.0250.0480.0960.153
TP_type_insd_pass_back0.019-0.006-0.0310.0590.0040.0190.1351.000NaN0.0590.0360.0360.009-0.003NaN-0.0020.020-0.0700.2430.1930.1310.1410.1890.0640.0320.0580.0290.077-0.0030.0430.1020.0050.0880.0260.1180.0660.100
TP_type_insd_pass_frontNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
TP_type_driver0.601-0.0120.0120.016-0.0010.2240.0730.059NaN1.0000.0980.163-0.088-0.017NaN0.028-0.027-0.7490.2710.1740.0320.0090.1880.0300.0490.0100.0390.0720.0350.0170.022-0.0250.0460.0680.0470.0990.268
TP_type_pass_back0.0300.003-0.0040.004-0.0130.0190.0220.036NaN0.0981.0000.296-0.013-0.003NaN-0.0020.076-0.0860.5360.178-0.001-0.0300.0330.0520.1310.2100.0620.1130.1170.0800.1310.0560.1190.1890.1390.1060.292
TP_type_pass_front0.027-0.0000.0060.0340.0070.0130.0890.036NaN0.1630.2961.000-0.0200.018NaN-0.0030.067-0.1260.5280.3020.060-0.1010.0060.0810.1180.0670.0730.1940.0660.0900.1000.0710.1430.1580.1750.1570.391
TP_type_bike0.0140.015-0.0100.0190.0020.003-0.0030.009NaN-0.088-0.013-0.0201.000-0.002NaN-0.001-0.001-0.0550.0400.0330.018-0.059-0.0150.011-0.0040.0050.0110.014-0.000-0.0010.0050.013-0.0100.016-0.0060.0560.083
TP_type_cyclist0.0150.0170.021-0.0010.001-0.0030.001-0.003NaN-0.017-0.0030.018-0.0021.000NaN-0.000-0.005-0.0150.0260.013-0.001-0.027-0.012-0.0040.056-0.002-0.003-0.004-0.004-0.002-0.0060.0110.014-0.005-0.005-0.003-0.003
TP_type_pass_multiNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
TP_type_pedestrian0.019-0.0020.0000.0050.0000.0060.021-0.002NaN0.028-0.002-0.003-0.001-0.000NaN1.000-0.002-0.008-0.0050.072-0.0010.0220.007-0.002-0.002-0.001-0.002-0.002-0.002-0.001-0.003-0.003-0.0020.094-0.0020.0130.046
TP_type_other-0.082-0.010-0.008-0.006-0.046-0.0000.0570.020NaN-0.0270.0760.067-0.001-0.005NaN-0.0021.000-0.1360.1890.0900.0730.3120.1470.0340.0800.0470.0140.1100.0580.0610.0760.0770.0840.0870.0860.2000.282
TP_type_nk-0.628-0.035-0.014-0.011-0.008-0.238-0.021-0.070NaN-0.749-0.086-0.126-0.055-0.015NaN-0.008-0.1361.000-0.237-0.132-0.0340.288-0.015-0.006-0.012-0.001-0.016-0.0360.001-0.0150.0190.042-0.037-0.020-0.018-0.075-0.213
TP_injury_whiplash0.0410.011-0.0020.049-0.0180.0320.1130.243NaN0.2710.5360.5280.0400.026NaN-0.0050.189-0.2371.0000.1090.008-0.338-0.0190.0610.1350.1420.1050.2320.1000.1020.1440.0820.1850.2280.2130.1270.500
TP_injury_traumatic0.077-0.009-0.0040.0220.0070.0150.1770.193NaN0.1740.1780.3020.0330.013NaN0.0720.090-0.1320.1091.0000.085-0.1610.0230.0700.1220.0310.0670.0770.0380.0430.1540.0340.0820.0770.1310.2530.352
TP_injury_fatality0.0020.019-0.0170.0060.002-0.0080.0310.131NaN0.032-0.0010.0600.018-0.001NaN-0.0010.073-0.0340.0080.0851.000-0.0060.0480.141-0.003-0.0060.0020.036-0.0090.0050.0330.003-0.0100.0280.0040.1930.110
TP_injury_unclear-0.086-0.062-0.0100.012-0.071-0.0160.0350.141NaN0.009-0.030-0.101-0.059-0.027NaN0.0220.3120.288-0.338-0.161-0.0061.0000.4220.0180.0130.016-0.028-0.0170.0160.0040.0220.0300.003-0.0050.0010.026-0.135
TP_injury_nk0.155-0.001-0.0070.002-0.0170.0520.0680.189NaN0.1880.0330.006-0.015-0.012NaN0.0070.147-0.015-0.0190.0230.0480.4221.000-0.094-0.088-0.043-0.067-0.106-0.096-0.054-0.162-0.171-0.086-0.107-0.1210.046-0.053
TP_region_eastang-0.032-0.0110.0260.0180.0050.0000.0290.064NaN0.0300.0520.0810.011-0.004NaN-0.0020.034-0.0060.0610.0700.1410.018-0.0941.000-0.022-0.011-0.022-0.031-0.017-0.017-0.038-0.043-0.030-0.031-0.0310.0710.102
TP_region_eastmid-0.0210.006-0.0020.0110.005-0.0090.0560.032NaN0.0490.1310.118-0.0040.056NaN-0.0020.080-0.0120.1350.122-0.0030.013-0.088-0.0221.000-0.016-0.024-0.019-0.024-0.019-0.043-0.047-0.024-0.018-0.0180.0570.079
TP_region_london0.0040.028-0.0030.0080.003-0.0020.0050.058NaN0.0100.2100.0670.005-0.002NaN-0.0010.047-0.0010.1420.031-0.0060.016-0.043-0.011-0.0161.000-0.013-0.0170.008-0.007-0.005-0.027-0.014-0.021-0.0170.0170.081
TP_region_north-0.010-0.004-0.0050.0250.004-0.0040.0460.029NaN0.0390.0620.0730.011-0.003NaN-0.0020.014-0.0160.1050.0670.002-0.028-0.067-0.022-0.024-0.0131.000-0.022-0.021-0.004-0.037-0.034-0.024-0.022-0.0190.0430.076
TP_region_northw-0.015-0.019-0.0080.0210.0050.0150.0330.077NaN0.0720.1130.1940.014-0.004NaN-0.0020.110-0.0360.2320.0770.036-0.017-0.106-0.031-0.019-0.017-0.0221.000-0.019-0.020-0.050-0.050-0.032-0.038-0.0210.0400.184
TP_region_outerldn-0.011-0.016-0.002-0.0020.004-0.0000.030-0.003NaN0.0350.1170.066-0.000-0.004NaN-0.0020.0580.0010.1000.038-0.0090.016-0.096-0.017-0.0240.008-0.021-0.0191.000-0.017-0.028-0.036-0.029-0.025-0.0270.0240.105
TP_region_scotland0.005-0.007-0.0150.0010.0030.0040.0630.043NaN0.0170.0800.090-0.001-0.002NaN-0.0010.061-0.0150.1020.0430.0050.004-0.054-0.017-0.019-0.007-0.004-0.020-0.0171.000-0.028-0.028-0.017-0.014-0.0230.0400.118
TP_region_southe-0.070-0.005-0.0110.005-0.026-0.0260.0280.102NaN0.0220.1310.1000.005-0.006NaN-0.0030.0760.0190.1440.1540.0330.022-0.162-0.038-0.043-0.005-0.037-0.050-0.028-0.0281.000-0.071-0.039-0.053-0.0570.1030.115
TP_region_southw-0.084-0.0240.0020.021-0.006-0.0100.0020.005NaN-0.0250.0560.0710.0130.011NaN-0.0030.0770.0420.0820.0340.0030.030-0.171-0.043-0.047-0.027-0.034-0.050-0.036-0.028-0.0711.000-0.045-0.043-0.0490.0340.058
TP_region_wales-0.019-0.010-0.0160.014-0.114-0.0030.0350.088NaN0.0460.1190.143-0.0100.014NaN-0.0020.084-0.0370.1850.082-0.0100.003-0.086-0.030-0.024-0.014-0.024-0.032-0.029-0.017-0.039-0.0451.000-0.037-0.0330.0390.111
TP_region_westmid-0.003-0.0110.0230.0130.0060.0120.0250.026NaN0.0680.1890.1580.016-0.005NaN0.0940.087-0.0200.2280.0770.028-0.005-0.107-0.031-0.018-0.021-0.022-0.038-0.025-0.014-0.053-0.043-0.0371.000-0.0370.0600.174
TP_region_yorkshire-0.023-0.014-0.0120.0120.006-0.0230.0480.118NaN0.0470.1390.175-0.006-0.005NaN-0.0020.086-0.0180.2130.1310.0040.001-0.121-0.031-0.018-0.017-0.019-0.021-0.027-0.023-0.057-0.049-0.033-0.0371.0000.0790.192
Incurred-0.0170.0040.0080.0080.005-0.0010.0960.066NaN0.0990.1060.1570.056-0.003NaN0.0130.200-0.0750.1270.2530.1930.0260.0460.0710.0570.0170.0430.0400.0240.0400.1030.0340.0390.0600.0791.0000.480
Capped Incurred-0.002-0.014-0.0020.0260.0140.0100.1530.100NaN0.2680.2920.3910.083-0.003NaN0.0460.282-0.2130.5000.3520.110-0.135-0.0530.1020.0790.0810.0760.1840.1050.1180.1150.0580.1110.1740.1920.4801.000
2025-07-08T18:22:32.454863image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Claim NumberNotification_periodInception_to_lossTime_hourVechile_registration_presentIncident_details_presentInjury_details_presentTP_type_insd_pass_backTP_type_insd_pass_frontTP_type_driverTP_type_pass_backTP_type_pass_frontTP_type_bikeTP_type_cyclistTP_type_pass_multiTP_type_pedestrianTP_type_otherTP_type_nkTP_injury_whiplashTP_injury_traumaticTP_injury_fatalityTP_injury_unclearTP_injury_nkTP_region_eastangTP_region_eastmidTP_region_londonTP_region_northTP_region_northwTP_region_outerldnTP_region_scotlandTP_region_southeTP_region_southwTP_region_walesTP_region_westmidTP_region_yorkshireIncurredCapped Incurred
Claim Number1.000-0.0580.029-0.0030.0110.222-0.0220.027NaN0.6460.0380.0330.0140.015NaN0.019-0.062-0.6540.0550.083-0.000-0.0840.170-0.033-0.027-0.011-0.015-0.014-0.015-0.006-0.087-0.096-0.029-0.002-0.0380.0030.003
Notification_period-0.0581.000-0.0110.184-0.026-0.0410.0120.042NaN-0.0520.0470.0490.0040.000NaN0.0110.0330.0300.0420.0500.054-0.0270.032-0.0090.0100.0140.0040.007-0.0120.015-0.029-0.0250.043-0.015-0.013-0.007-0.007
Inception_to_loss0.029-0.0111.000-0.0080.0110.013-0.013-0.024NaN0.014-0.0010.009-0.0120.021NaN0.001-0.004-0.014-0.012-0.005-0.017-0.0010.0010.0230.0000.002-0.006-0.012-0.010-0.010-0.0110.008-0.0140.017-0.016-0.014-0.014
Time_hour-0.0030.184-0.0081.000-0.0100.0160.0950.057NaN0.0170.0160.0420.019-0.002NaN0.005-0.010-0.0200.0510.0350.009-0.009-0.010-0.0020.0180.0090.0200.022-0.0000.003-0.0010.0090.0070.0060.0130.0100.010
Vechile_registration_present0.011-0.0260.011-0.0101.000-0.0140.0040.004NaN0.011-0.0220.0070.0020.001NaN0.000-0.014-0.010-0.0010.0070.002-0.026-0.0110.0050.0060.0040.0040.0060.0050.003-0.024-0.009-0.0410.0070.0070.0110.011
Incident_details_present0.222-0.0410.0130.016-0.0141.0000.0980.029NaN0.2380.0150.0130.005-0.003NaN0.0060.003-0.2440.0340.018-0.011-0.0190.050-0.006-0.015-0.019-0.0110.018-0.004-0.001-0.031-0.013-0.0100.019-0.0220.0230.023
Injury_details_present-0.0220.012-0.0130.0950.0040.0981.0000.144NaN0.0680.0330.087-0.0020.001NaN0.0210.062-0.0320.1350.1860.0370.0200.0340.0100.0480.0080.0390.0410.0140.0570.019-0.0120.0370.0200.0390.0470.047
TP_type_insd_pass_back0.0270.042-0.0240.0570.0040.0290.1441.000NaN0.0690.0600.0480.010-0.003NaN-0.0020.035-0.0810.1930.1750.0870.0840.0980.0370.0220.0110.0230.0330.0010.0270.025-0.0010.0460.0170.0630.0470.047
TP_type_insd_pass_frontNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
TP_type_driver0.646-0.0520.0140.0170.0110.2380.0680.069NaN1.0000.1110.168-0.093-0.017NaN0.022-0.011-0.8230.3070.1770.031-0.0730.1470.0100.0250.0030.0240.0600.0170.008-0.025-0.0520.0260.0520.0160.2260.226
TP_type_pass_back0.0380.047-0.0010.016-0.0220.0150.0330.060NaN0.1111.0000.347-0.015-0.004NaN-0.0020.081-0.1070.3600.1690.003-0.080-0.0200.0310.0370.0700.0340.0860.0420.0100.0390.0100.0410.0560.0700.2240.224
TP_type_pass_front0.0330.0490.0090.0420.0070.0130.0870.048NaN0.1680.3471.000-0.0200.019NaN-0.0030.086-0.1360.4350.2600.054-0.151-0.0420.0290.0510.0370.0440.1230.0210.0580.0290.0180.0660.0840.0830.2990.299
TP_type_bike0.0140.004-0.0120.0190.0020.005-0.0020.010NaN-0.093-0.015-0.0201.000-0.002NaN-0.0010.006-0.0590.0600.0420.022-0.071-0.0230.020-0.0010.0140.0090.0100.0030.0020.0100.022-0.0100.013-0.0020.0760.076
TP_type_cyclist0.0150.0000.021-0.0020.001-0.0030.001-0.003NaN-0.017-0.0040.019-0.0021.000NaN-0.000-0.006-0.0160.0330.018-0.001-0.031-0.013-0.0040.025-0.003-0.003-0.005-0.004-0.003-0.0070.0140.023-0.006-0.0060.0020.002
TP_type_pass_multiNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
TP_type_pedestrian0.0190.0110.0010.0050.0000.0060.021-0.002NaN0.022-0.002-0.003-0.001-0.000NaN1.000-0.003-0.008-0.0050.047-0.0010.0240.009-0.002-0.002-0.001-0.002-0.003-0.002-0.001-0.004-0.003-0.0020.049-0.0030.0220.022
TP_type_other-0.0620.033-0.004-0.010-0.0140.0030.0620.035NaN-0.0110.0810.0860.006-0.006NaN-0.0031.000-0.1640.1990.1150.0550.2000.0790.0170.0580.0140.0300.0490.0200.0480.0560.0170.0510.0290.0720.2180.218
TP_type_nk-0.6540.030-0.014-0.020-0.010-0.244-0.032-0.081NaN-0.823-0.107-0.136-0.059-0.016NaN-0.008-0.1641.000-0.296-0.153-0.0360.285-0.0330.003-0.0050.002-0.012-0.0370.010-0.0060.0390.060-0.020-0.019-0.002-0.224-0.224
TP_injury_whiplash0.0550.042-0.0120.051-0.0010.0340.1350.193NaN0.3070.3600.4350.0600.033NaN-0.0050.199-0.2961.0000.1020.016-0.483-0.1330.0340.0790.0590.0740.1550.0620.0660.0860.0550.1100.1180.1110.4900.491
TP_injury_traumatic0.0830.050-0.0050.0350.0070.0180.1860.175NaN0.1770.1690.2600.0420.018NaN0.0470.115-0.1530.1021.0000.078-0.229-0.0340.0460.0600.0390.0590.0540.0240.0210.0640.0070.0500.0410.0720.2460.246
TP_injury_fatality-0.0000.054-0.0170.0090.002-0.0110.0370.087NaN0.0310.0030.0540.022-0.001NaN-0.0010.055-0.0360.0160.0781.000-0.0170.0420.054-0.001-0.0070.0070.009-0.0100.011-0.010-0.008-0.0120.035-0.0050.0530.052
TP_injury_unclear-0.084-0.027-0.001-0.009-0.026-0.0190.0200.084NaN-0.073-0.080-0.151-0.071-0.031NaN0.0240.2000.285-0.483-0.229-0.0171.0000.3810.008-0.007-0.016-0.036-0.0660.007-0.001-0.017-0.007-0.046-0.027-0.022-0.279-0.279
TP_injury_nk0.1700.0320.001-0.010-0.0110.0500.0340.098NaN0.147-0.020-0.042-0.023-0.013NaN0.0090.079-0.033-0.133-0.0340.0420.3811.000-0.130-0.139-0.094-0.095-0.148-0.126-0.083-0.250-0.236-0.140-0.165-0.176-0.141-0.141
TP_region_eastang-0.033-0.0090.023-0.0020.005-0.0060.0100.037NaN0.0100.0310.0290.020-0.004NaN-0.0020.0170.0030.0340.0460.0540.008-0.1301.000-0.029-0.012-0.027-0.041-0.023-0.023-0.048-0.053-0.039-0.041-0.0390.0540.054
TP_region_eastmid-0.0270.0100.0000.0180.006-0.0150.0480.022NaN0.0250.0370.051-0.0010.025NaN-0.0020.058-0.0050.0790.060-0.001-0.007-0.139-0.0291.000-0.021-0.030-0.030-0.032-0.026-0.061-0.059-0.040-0.024-0.0320.0350.035
TP_region_london-0.0110.0140.0020.0090.004-0.0190.0080.011NaN0.0030.0700.0370.014-0.003NaN-0.0010.0140.0020.0590.039-0.007-0.016-0.094-0.012-0.0211.000-0.019-0.0240.008-0.008-0.013-0.037-0.022-0.031-0.0240.0350.035
TP_region_north-0.0150.004-0.0060.0200.004-0.0110.0390.023NaN0.0240.0340.0440.009-0.003NaN-0.0020.030-0.0120.0740.0590.007-0.036-0.095-0.027-0.030-0.0191.000-0.025-0.0260.004-0.047-0.039-0.031-0.032-0.0180.0540.055
TP_region_northw-0.0140.007-0.0120.0220.0060.0180.0410.033NaN0.0600.0860.1230.010-0.005NaN-0.0030.049-0.0370.1550.0540.009-0.066-0.148-0.041-0.030-0.024-0.0251.000-0.036-0.028-0.066-0.063-0.042-0.049-0.0410.1060.106
TP_region_outerldn-0.015-0.012-0.010-0.0000.005-0.0040.0140.001NaN0.0170.0420.0210.003-0.004NaN-0.0020.0200.0100.0620.024-0.0100.007-0.126-0.023-0.0320.008-0.026-0.0361.000-0.022-0.034-0.040-0.038-0.033-0.0380.0540.054
TP_region_scotland-0.0060.015-0.0100.0030.003-0.0010.0570.027NaN0.0080.0100.0580.002-0.003NaN-0.0010.048-0.0060.0660.0210.011-0.001-0.083-0.023-0.026-0.0080.004-0.028-0.0221.000-0.037-0.036-0.021-0.025-0.0310.0730.074
TP_region_southe-0.087-0.029-0.011-0.001-0.024-0.0310.0190.025NaN-0.0250.0390.0290.010-0.007NaN-0.0040.0560.0390.0860.064-0.010-0.017-0.250-0.048-0.061-0.013-0.047-0.066-0.034-0.0371.000-0.089-0.060-0.074-0.0770.0680.068
TP_region_southw-0.096-0.0250.0080.009-0.009-0.013-0.012-0.001NaN-0.0520.0100.0180.0220.014NaN-0.0030.0170.0600.0550.007-0.008-0.007-0.236-0.053-0.059-0.037-0.039-0.063-0.040-0.036-0.0891.000-0.060-0.062-0.0610.0450.044
TP_region_wales-0.0290.043-0.0140.007-0.041-0.0100.0370.046NaN0.0260.0410.066-0.0100.023NaN-0.0020.051-0.0200.1100.050-0.012-0.046-0.140-0.039-0.040-0.022-0.031-0.042-0.038-0.021-0.060-0.0601.000-0.049-0.0460.0900.090
TP_region_westmid-0.002-0.0150.0170.0060.0070.0190.0200.017NaN0.0520.0560.0840.013-0.006NaN0.0490.029-0.0190.1180.0410.035-0.027-0.165-0.041-0.024-0.031-0.032-0.049-0.033-0.025-0.074-0.062-0.0491.000-0.0540.0860.086
TP_region_yorkshire-0.038-0.013-0.0160.0130.007-0.0220.0390.063NaN0.0160.0700.083-0.002-0.006NaN-0.0030.072-0.0020.1110.072-0.005-0.022-0.176-0.039-0.032-0.024-0.018-0.041-0.038-0.031-0.077-0.061-0.046-0.0541.0000.1030.103
Incurred0.003-0.007-0.0140.0100.0110.0230.0470.047NaN0.2260.2240.2990.0760.002NaN0.0220.218-0.2240.4900.2460.053-0.279-0.1410.0540.0350.0350.0540.1060.0540.0730.0680.0450.0900.0860.1031.0001.000
Capped Incurred0.003-0.007-0.0140.0100.0110.0230.0470.047NaN0.2260.2240.2990.0760.002NaN0.0220.218-0.2240.4910.2460.052-0.279-0.1410.0540.0350.0350.0550.1060.0540.0740.0680.0440.0900.0860.1031.0001.000
2025-07-08T18:22:32.578005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Claim NumberNotification_periodInception_to_lossTime_hourVechile_registration_presentIncident_details_presentInjury_details_presentTP_type_insd_pass_backTP_type_insd_pass_frontTP_type_driverTP_type_pass_backTP_type_pass_frontTP_type_bikeTP_type_cyclistTP_type_pass_multiTP_type_pedestrianTP_type_otherTP_type_nkTP_injury_whiplashTP_injury_traumaticTP_injury_fatalityTP_injury_unclearTP_injury_nkTP_region_eastangTP_region_eastmidTP_region_londonTP_region_northTP_region_northwTP_region_outerldnTP_region_scotlandTP_region_southeTP_region_southwTP_region_walesTP_region_westmidTP_region_yorkshireIncurredCapped Incurred
Claim Number1.000-0.0430.019-0.0020.0090.181-0.0180.022NaN0.5210.0310.0270.0110.012NaN0.015-0.051-0.5310.0440.067-0.000-0.0670.136-0.027-0.022-0.009-0.012-0.011-0.012-0.005-0.070-0.078-0.024-0.002-0.0310.0010.001
Notification_period-0.0431.000-0.0080.147-0.024-0.0380.0110.038NaN-0.0470.0430.0450.0030.000NaN0.0100.0300.0270.0370.0450.049-0.0240.028-0.0080.0090.0130.0040.007-0.0110.013-0.026-0.0220.039-0.013-0.012-0.005-0.005
Inception_to_loss0.019-0.0081.000-0.0060.0090.011-0.010-0.020NaN0.011-0.0010.007-0.0090.017NaN0.001-0.004-0.011-0.010-0.004-0.014-0.0010.0010.0190.0000.001-0.005-0.009-0.009-0.008-0.0090.007-0.0110.014-0.013-0.010-0.010
Time_hour-0.0020.147-0.0061.000-0.0090.0130.0800.048NaN0.0150.0130.0350.016-0.001NaN0.004-0.008-0.0160.0420.0290.007-0.007-0.009-0.0020.0150.0070.0170.018-0.0000.002-0.0010.0070.0060.0050.0110.0060.006
Vechile_registration_present0.009-0.0240.009-0.0091.000-0.0140.0040.004NaN0.011-0.0220.0070.0020.001NaN0.000-0.014-0.010-0.0010.0070.002-0.025-0.0110.0050.0060.0040.0040.0060.0050.003-0.024-0.009-0.0400.0070.0070.0090.009
Incident_details_present0.181-0.0380.0110.013-0.0141.0000.0980.029NaN0.2350.0150.0130.005-0.003NaN0.0060.003-0.2420.0330.018-0.011-0.0190.049-0.006-0.015-0.019-0.0110.018-0.004-0.001-0.031-0.013-0.0100.019-0.0220.0190.019
Injury_details_present-0.0180.011-0.0100.0800.0040.0981.0000.144NaN0.0670.0330.087-0.0020.001NaN0.0210.062-0.0320.1320.1850.0370.0200.0340.0100.0480.0080.0390.0400.0140.0570.019-0.0120.0370.0200.0390.0380.038
TP_type_insd_pass_back0.0220.038-0.0200.0480.0040.0290.1441.000NaN0.0680.0600.0480.010-0.003NaN-0.0020.035-0.0800.1890.1740.0860.0810.0960.0360.0220.0110.0230.0330.0010.0260.025-0.0010.0460.0170.0620.0380.038
TP_type_insd_pass_frontNaNNaNNaNNaNNaNNaNNaNNaN1.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
TP_type_driver0.521-0.0470.0110.0150.0110.2350.0670.068NaN1.0000.1100.166-0.092-0.017NaN0.022-0.011-0.8070.2970.1750.030-0.0710.1430.0100.0240.0030.0230.0590.0160.008-0.025-0.0510.0250.0510.0160.1830.183
TP_type_pass_back0.0310.043-0.0010.013-0.0220.0150.0330.060NaN0.1101.0000.345-0.015-0.004NaN-0.0020.080-0.1060.3530.1670.003-0.077-0.0200.0310.0360.0700.0340.0850.0420.0100.0380.0100.0400.0550.0700.1820.183
TP_type_pass_front0.0270.0450.0070.0350.0070.0130.0870.048NaN0.1660.3451.000-0.0200.019NaN-0.0030.085-0.1350.4260.2590.054-0.147-0.0410.0290.0510.0370.0440.1220.0210.0580.0290.0180.0660.0840.0830.2440.244
TP_type_bike0.0110.003-0.0090.0160.0020.005-0.0020.010NaN-0.092-0.015-0.0201.000-0.002NaN-0.0010.006-0.0580.0580.0420.022-0.069-0.0220.020-0.0010.0140.0090.0100.0030.0020.0100.021-0.0100.013-0.0020.0620.062
TP_type_cyclist0.0120.0000.017-0.0010.001-0.0030.001-0.003NaN-0.017-0.0040.019-0.0021.000NaN-0.000-0.006-0.0160.0320.017-0.001-0.030-0.013-0.0040.025-0.003-0.003-0.005-0.004-0.003-0.0070.0140.023-0.006-0.0060.0020.002
TP_type_pass_multiNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
TP_type_pedestrian0.0150.0100.0010.0040.0000.0060.021-0.002NaN0.022-0.002-0.003-0.001-0.000NaN1.000-0.003-0.008-0.0050.047-0.0010.0230.009-0.002-0.002-0.001-0.002-0.003-0.002-0.001-0.004-0.003-0.0020.049-0.0030.0180.018
TP_type_other-0.0510.030-0.004-0.008-0.0140.0030.0620.035NaN-0.0110.0800.0850.006-0.006NaN-0.0031.000-0.1610.1940.1140.0550.1940.0770.0170.0570.0140.0300.0490.0200.0480.0560.0170.0510.0290.0710.1770.177
TP_type_nk-0.5310.027-0.011-0.016-0.010-0.242-0.032-0.080NaN-0.807-0.106-0.135-0.058-0.016NaN-0.008-0.1611.000-0.287-0.151-0.0360.280-0.0320.003-0.0050.002-0.012-0.0360.010-0.0060.0380.059-0.020-0.019-0.002-0.181-0.181
TP_injury_whiplash0.0440.037-0.0100.042-0.0010.0330.1320.189NaN0.2970.3530.4260.0580.032NaN-0.0050.194-0.2871.0000.1000.015-0.462-0.1280.0330.0770.0580.0730.1520.0610.0650.0850.0540.1080.1160.1080.3970.398
TP_injury_traumatic0.0670.045-0.0040.0290.0070.0180.1850.174NaN0.1750.1670.2590.0420.017NaN0.0470.114-0.1510.1001.0000.078-0.223-0.0330.0460.0590.0390.0590.0530.0240.0200.0640.0070.0490.0400.0720.2000.200
TP_injury_fatality-0.0000.049-0.0140.0070.002-0.0110.0370.086NaN0.0300.0030.0540.022-0.001NaN-0.0010.055-0.0360.0150.0781.000-0.0160.0410.054-0.001-0.0070.0070.009-0.0100.011-0.010-0.008-0.0120.035-0.0050.0430.043
TP_injury_unclear-0.067-0.024-0.001-0.007-0.025-0.0190.0200.081NaN-0.071-0.077-0.147-0.069-0.030NaN0.0230.1940.280-0.462-0.223-0.0161.0000.3710.007-0.007-0.016-0.035-0.0640.007-0.001-0.016-0.007-0.045-0.027-0.021-0.223-0.223
TP_injury_nk0.1360.0280.001-0.009-0.0110.0490.0340.096NaN0.143-0.020-0.041-0.022-0.013NaN0.0090.077-0.032-0.128-0.0330.0410.3711.000-0.127-0.136-0.092-0.093-0.145-0.123-0.081-0.243-0.230-0.136-0.160-0.171-0.113-0.113
TP_region_eastang-0.027-0.0080.019-0.0020.005-0.0060.0100.036NaN0.0100.0310.0290.020-0.004NaN-0.0020.0170.0030.0330.0460.0540.007-0.1271.000-0.029-0.012-0.027-0.040-0.023-0.023-0.048-0.052-0.038-0.040-0.0390.0440.044
TP_region_eastmid-0.0220.0090.0000.0150.006-0.0150.0480.022NaN0.0240.0360.051-0.0010.025NaN-0.0020.057-0.0050.0770.059-0.001-0.007-0.136-0.0291.000-0.021-0.030-0.030-0.032-0.025-0.061-0.059-0.040-0.024-0.0310.0280.028
TP_region_london-0.0090.0130.0010.0070.004-0.0190.0080.011NaN0.0030.0700.0370.014-0.003NaN-0.0010.0140.0020.0580.039-0.007-0.016-0.092-0.012-0.0211.000-0.019-0.0240.008-0.008-0.012-0.037-0.022-0.031-0.0230.0290.029
TP_region_north-0.0120.004-0.0050.0170.004-0.0110.0390.023NaN0.0230.0340.0440.009-0.003NaN-0.0020.030-0.0120.0730.0590.007-0.035-0.093-0.027-0.030-0.0191.000-0.025-0.0260.003-0.047-0.039-0.031-0.032-0.0180.0440.044
TP_region_northw-0.0110.007-0.0090.0180.0060.0180.0400.033NaN0.0590.0850.1220.010-0.005NaN-0.0030.049-0.0360.1520.0530.009-0.064-0.145-0.040-0.030-0.024-0.0251.000-0.036-0.028-0.065-0.063-0.042-0.049-0.0410.0860.087
TP_region_outerldn-0.012-0.011-0.009-0.0000.005-0.0040.0140.001NaN0.0160.0420.0210.003-0.004NaN-0.0020.0200.0100.0610.024-0.0100.007-0.123-0.023-0.0320.008-0.026-0.0361.000-0.022-0.034-0.040-0.038-0.033-0.0380.0440.044
TP_region_scotland-0.0050.013-0.0080.0020.003-0.0010.0570.026NaN0.0080.0100.0580.002-0.003NaN-0.0010.048-0.0060.0650.0200.011-0.001-0.081-0.023-0.025-0.0080.003-0.028-0.0221.000-0.036-0.036-0.021-0.025-0.0310.0600.060
TP_region_southe-0.070-0.026-0.009-0.001-0.024-0.0310.0190.025NaN-0.0250.0380.0290.010-0.007NaN-0.0040.0560.0380.0850.064-0.010-0.016-0.243-0.048-0.061-0.012-0.047-0.065-0.034-0.0361.000-0.088-0.059-0.073-0.0760.0560.056
TP_region_southw-0.078-0.0220.0070.007-0.009-0.013-0.012-0.001NaN-0.0510.0100.0180.0210.014NaN-0.0030.0170.0590.0540.007-0.008-0.007-0.230-0.052-0.059-0.037-0.039-0.063-0.040-0.036-0.0881.000-0.059-0.061-0.0610.0360.036
TP_region_wales-0.0240.039-0.0110.006-0.040-0.0100.0370.046NaN0.0250.0400.066-0.0100.023NaN-0.0020.051-0.0200.1080.049-0.012-0.045-0.136-0.038-0.040-0.022-0.031-0.042-0.038-0.021-0.059-0.0591.000-0.049-0.0450.0740.074
TP_region_westmid-0.002-0.0130.0140.0050.0070.0190.0200.017NaN0.0510.0550.0840.013-0.006NaN0.0490.029-0.0190.1160.0400.035-0.027-0.160-0.040-0.024-0.031-0.032-0.049-0.033-0.025-0.073-0.061-0.0491.000-0.0530.0700.070
TP_region_yorkshire-0.031-0.012-0.0130.0110.007-0.0220.0390.062NaN0.0160.0700.083-0.002-0.006NaN-0.0030.071-0.0020.1080.072-0.005-0.021-0.171-0.039-0.031-0.023-0.018-0.041-0.038-0.031-0.076-0.061-0.045-0.0531.0000.0840.084
Incurred0.001-0.005-0.0100.0060.0090.0190.0380.038NaN0.1830.1820.2440.0620.002NaN0.0180.177-0.1810.3970.2000.043-0.223-0.1130.0440.0280.0290.0440.0860.0440.0600.0560.0360.0740.0700.0841.0000.999
Capped Incurred0.001-0.005-0.0100.0060.0090.0190.0380.038NaN0.1830.1830.2440.0620.002NaN0.0180.177-0.1810.3980.2000.043-0.223-0.1130.0440.0280.0290.0440.0870.0440.0600.0560.0360.0740.0700.0840.9991.000
2025-07-08T18:22:32.706150image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Claim NumberNotifierNotification_periodInception_to_lossLocation_of_incidentWeather_conditionsVehicle_mobileTime_hourMain_driverPH_considered_TP_at_faultVechile_registration_presentIncident_details_presentInjury_details_presentTP_type_insd_pass_backTP_type_driverTP_type_pass_backTP_type_pass_frontTP_type_bikeTP_type_cyclistTP_type_pedestrianTP_type_otherTP_type_nkTP_injury_whiplashTP_injury_traumaticTP_injury_fatalityTP_injury_unclearTP_injury_nkTP_region_eastangTP_region_eastmidTP_region_londonTP_region_northTP_region_northwTP_region_outerldnTP_region_scotlandTP_region_southeTP_region_southwTP_region_walesTP_region_westmidTP_region_yorkshireIncurredCapped Incurred
Claim Number1.0000.7460.0510.0000.1160.3890.2020.0580.6910.7460.0190.3910.0850.0960.5600.0380.0740.0000.0000.0010.0900.5460.0940.1350.0450.1050.1410.0000.0000.0200.0230.0000.0000.0000.0890.0850.0340.0000.0390.0000.083
Notifier0.7461.0000.2360.0000.2490.2680.3760.3270.2680.1540.0130.0980.0760.0000.1330.0660.0420.0000.0000.0000.0470.1130.0510.0240.0000.0670.0600.0110.0640.0000.0000.0340.0000.0000.0650.0260.0540.0520.0300.0210.111
Notification_period0.0510.2361.0000.0000.1900.1930.3440.2500.0970.0210.0000.0590.0670.0000.0000.0000.0000.0580.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0970.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Inception_to_loss0.0000.0000.0001.0000.0000.0020.0000.0310.0000.0000.0000.0260.0000.0000.0000.0100.0000.0000.0000.0090.0000.0000.0190.0570.0000.0000.0000.0000.0000.0060.0000.0000.0230.0000.0110.0000.0000.0350.0000.0350.000
Location_of_incident0.1160.2490.1900.0001.0000.4680.4780.3080.1950.0960.0240.0660.2280.0350.0770.0220.0720.0000.0480.0000.0730.0510.0940.0670.0480.1480.0540.0000.0500.1100.0000.1250.0190.0000.1520.0670.0690.0000.0230.1240.103
Weather_conditions0.3890.2680.1930.0020.4681.0000.3490.4040.1050.1050.0490.0730.1150.0000.0510.0370.0040.0000.0000.0000.0350.0710.0430.0000.0420.0810.0330.0370.0000.0330.0000.0000.0000.0500.0320.0500.0330.0000.0270.0430.035
Vehicle_mobile0.2020.3760.3440.0000.4780.3491.0000.5290.4600.0980.0040.0420.2290.0810.2050.1080.2250.0000.0000.0000.1170.1640.2000.1400.1280.2280.1390.0960.0540.0840.0520.0290.0000.0790.1410.0760.0890.0460.0880.0770.265
Time_hour0.0580.3270.2500.0310.3080.4040.5291.0000.1990.0000.0000.0500.1780.0730.0440.0270.0880.0000.0000.0000.0630.0230.0730.1400.0310.0830.0520.0370.0400.1570.1100.0290.0420.0000.0610.0480.0290.0340.0000.0150.145
Main_driver0.6910.2680.0970.0000.1950.1050.4600.1991.0000.5100.0120.1020.0160.0290.7150.0000.0670.0660.0070.0000.0860.6930.0410.0750.0000.0800.1550.0530.0000.0190.0400.0000.0270.0000.1080.1510.0210.0520.0510.0000.028
PH_considered_TP_at_fault0.7460.1540.0210.0000.0960.1050.0980.0000.5101.0000.0000.3200.0290.0360.5690.0000.0400.0000.0000.0000.0660.5470.1050.0790.0000.1200.1240.0000.0000.0000.0000.0000.0000.0000.0610.0750.0020.0000.0260.0000.053
Vechile_registration_present0.0190.0130.0000.0000.0240.0490.0040.0000.0120.0001.0000.0000.0000.0000.1170.0060.0000.0000.0000.0000.1530.0000.1030.0000.0000.3870.0450.0000.0000.0000.0000.0000.0000.0000.0350.0000.3810.0000.0000.0000.000
Incident_details_present0.3910.0980.0590.0260.0660.0730.0420.0500.1020.3200.0001.0000.1520.0220.3430.0000.0000.0000.0000.0000.0150.3380.0390.0160.0010.0180.0440.0000.0180.0050.0060.0250.0000.0000.0280.0000.0000.0130.0060.0000.034
Injury_details_present0.0850.0760.0670.0000.2280.1150.2290.1780.0160.0290.0000.1521.0000.1170.1210.0260.0520.0000.0000.0000.0570.0840.1780.1540.0230.1980.1340.0700.0550.0000.0370.0570.0510.0860.0360.0490.0290.0200.0780.0700.215
TP_type_insd_pass_back0.0960.0000.0000.0000.0350.0000.0810.0730.0290.0360.0000.0220.1171.0000.0380.0490.0520.0000.0000.0000.0000.0470.2400.3600.1670.3800.3300.1330.0640.3260.1160.2240.0000.0800.3240.0270.1460.0450.1420.1190.120
TP_type_driver0.5600.1330.0000.0000.0770.0510.2050.0440.7150.5690.1170.3430.1210.0381.0000.0920.2760.1500.0000.0990.0820.7680.2530.1540.0780.5500.3410.0660.0640.0000.0360.0810.1280.0000.2190.0920.0540.0780.1140.1970.265
TP_type_pass_back0.0380.0660.0000.0100.0220.0370.1080.0270.0000.0000.0060.0000.0260.0490.0921.0000.3530.0000.0000.0000.1280.0690.7170.1980.0000.1600.3180.0940.4460.6470.1450.1680.2360.1830.4370.1840.3360.7170.2210.1230.283
TP_type_pass_front0.0740.0420.0000.0000.0720.0040.2250.0880.0670.0400.0000.0000.0520.0520.2760.3531.0000.0000.0070.0000.1030.2220.5800.2950.1730.2800.1630.2710.2010.1000.1030.3250.3010.2100.3290.2940.2840.2450.3670.1470.435
TP_type_bike0.0000.0000.0580.0000.0000.0000.0000.0000.0660.0000.0000.0000.0000.0000.1500.0000.0001.0000.0000.0000.0000.0800.0830.0430.0170.0780.0150.0000.0000.0000.0000.0870.0000.0000.0000.0000.0000.0930.0000.0830.163
TP_type_cyclist0.0000.0000.0490.0000.0480.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0070.0001.0000.0000.0000.0000.0290.0000.0000.0230.0000.0000.1380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
TP_type_pedestrian0.0010.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0990.0000.0000.0000.0001.0000.0000.0000.0000.0990.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2020.0000.0000.066
TP_type_other0.0900.0470.0000.0000.0730.0350.1170.0630.0860.0660.1530.0150.0570.0000.0820.1280.1030.0000.0000.0001.0000.1140.2320.1050.1260.5760.3410.0550.1760.2160.0000.6440.1030.0710.0950.3950.4570.3650.1000.2650.262
TP_type_nk0.5460.1130.0000.0000.0510.0710.1640.0230.6930.5470.0000.3380.0840.0470.7680.0690.2220.0800.0000.0000.1141.0000.2320.1080.0020.6810.6450.0600.0220.0000.0000.1880.0400.0000.1140.1550.0170.0710.1410.0000.230
TP_injury_whiplash0.0940.0510.0000.0190.0940.0430.2000.0730.0410.1050.1030.0390.1780.2400.2530.7170.5800.0830.0290.0000.2320.2321.0000.1590.0210.5900.2880.0950.2340.6610.1780.5510.2210.2750.2990.1610.2710.5550.3220.0000.464
TP_injury_traumatic0.1350.0240.0000.0570.0670.0000.1400.1400.0750.0790.0000.0160.1540.3600.1540.1980.2950.0430.0000.0990.1050.1080.1591.0000.1220.2520.1380.1110.1830.0370.5830.2980.0690.0950.4600.0860.1130.1030.1510.3150.425
TP_injury_fatality0.0450.0000.0000.0000.0480.0420.1280.0310.0000.0000.0000.0010.0230.1670.0780.0000.1730.0170.0000.0000.1260.0020.0210.1221.0000.0440.1020.8330.0000.0000.0000.1550.0000.0000.4480.0860.0000.0430.0500.3720.184
TP_injury_unclear0.1050.0670.0000.0000.1480.0810.2280.0830.0800.1200.3870.0180.1980.3800.5500.1600.2800.0780.0230.0460.5760.6810.5900.2520.0441.0000.6500.0720.1050.3050.0620.4710.0620.0440.1360.5230.4950.1060.1110.2170.387
TP_injury_nk0.1410.0600.0000.0000.0540.0330.1390.0520.1550.1240.0450.0440.1340.3300.3410.3180.1630.0150.0000.0000.3410.6450.2880.1380.1020.6501.0000.0950.1720.0840.0730.1110.0910.0650.2090.1950.2300.2320.1420.2410.208
TP_region_eastang0.0000.0110.0000.0000.0000.0370.0960.0370.0530.0000.0000.0000.0700.1330.0660.0940.2710.0000.0000.0000.0550.0600.0950.1110.8330.0720.0951.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3040.131
TP_region_eastmid0.0000.0640.0000.0000.0500.0000.0540.0400.0000.0000.0000.0180.0550.0640.0640.4460.2010.0000.1380.0000.1760.0220.2340.1830.0000.1050.1720.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1810.122
TP_region_london0.0200.0000.0970.0060.1100.0330.0840.1570.0190.0000.0000.0050.0000.3260.0000.6470.1000.0000.0000.0000.2160.0000.6610.0370.0000.3050.0840.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.126
TP_region_north0.0230.0000.0000.0000.0000.0000.0520.1100.0400.0000.0000.0060.0370.1160.0360.1450.1030.0000.0000.0000.0000.0000.1780.5830.0000.0620.0730.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.1620.144
TP_region_northw0.0000.0340.0000.0000.1250.0000.0290.0290.0000.0000.0000.0250.0570.2240.0810.1680.3250.0870.0000.0000.6440.1880.5510.2980.1550.4710.1110.0000.0000.0000.0001.0000.0350.0000.0000.0000.0000.0000.0060.0930.214
TP_region_outerldn0.0000.0000.0000.0230.0190.0000.0000.0420.0270.0000.0000.0000.0510.0000.1280.2360.3010.0000.0000.0000.1030.0400.2210.0690.0000.0620.0910.0000.0000.0000.0000.0351.0000.0000.0000.0000.0000.0000.0000.0000.130
TP_region_scotland0.0000.0000.0000.0000.0000.0500.0790.0000.0000.0000.0000.0000.0860.0800.0000.1830.2100.0000.0000.0000.0710.0000.2750.0950.0000.0440.0650.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0980.128
TP_region_southe0.0890.0650.0000.0110.1520.0320.1410.0610.1080.0610.0350.0280.0360.3240.2190.4370.3290.0000.0000.0000.0950.1140.2990.4600.4480.1360.2090.0000.0000.0000.0000.0000.0000.0001.0000.0470.0000.0050.0270.2940.181
TP_region_southw0.0850.0260.0000.0000.0670.0500.0760.0480.1510.0750.0000.0000.0490.0270.0920.1840.2940.0000.0000.0000.3950.1550.1610.0860.0860.5230.1950.0000.0000.0000.0000.0000.0000.0000.0471.0000.0000.0460.0290.0640.171
TP_region_wales0.0340.0540.0000.0000.0690.0330.0890.0290.0210.0020.3810.0000.0290.1460.0540.3360.2840.0000.0000.0000.4570.0170.2710.1130.0000.4950.2300.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.157
TP_region_westmid0.0000.0520.0000.0350.0000.0000.0460.0340.0520.0000.0000.0130.0200.0450.0780.7170.2450.0930.0000.2020.3650.0710.5550.1030.0430.1060.2320.0000.0000.0000.0000.0000.0000.0000.0050.0460.0001.0000.0000.0000.182
TP_region_yorkshire0.0390.0300.0000.0000.0230.0270.0880.0000.0510.0260.0000.0060.0780.1420.1140.2210.3670.0000.0000.0000.1000.1410.3220.1510.0500.1110.1420.0000.0000.0000.0000.0060.0000.0000.0270.0290.0000.0001.0000.1010.208
Incurred0.0000.0210.0000.0350.1240.0430.0770.0150.0000.0000.0000.0000.0700.1190.1970.1230.1470.0830.0000.0000.2650.0000.0000.3150.3720.2170.2410.3040.1810.0000.1620.0930.0000.0980.2940.0640.0000.0000.1011.0000.323
Capped Incurred0.0830.1110.0000.0000.1030.0350.2650.1450.0280.0530.0000.0340.2150.1200.2650.2830.4350.1630.0000.0660.2620.2300.4640.4250.1840.3870.2080.1310.1220.1260.1440.2140.1300.1280.1810.1710.1570.1820.2080.3231.000
2025-07-08T18:22:32.817784image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Incident_details_presentInjury_details_presentLocation_of_incidentMain_driverNotifierPH_considered_TP_at_faultTP_injury_fatalityTP_injury_traumaticTP_region_northTP_type_bikeTP_type_cyclistTP_type_insd_pass_backTP_type_pass_frontTP_type_pedestrianVechile_registration_presentVehicle_mobileWeather_conditions
Incident_details_present1.0000.0970.0500.1680.1200.2140.0020.0200.0070.0000.0000.0270.0000.0000.0000.0700.048
Injury_details_present0.0971.0000.1710.0260.0930.0190.0390.1880.0460.0000.0000.1430.0870.0000.0000.3750.076
Location_of_incident0.0500.1711.0000.1250.1550.0430.0300.0410.0000.0000.0360.0220.0460.0000.0180.3460.224
Main_driver0.1680.0260.1251.0000.2100.5100.0000.0560.0300.0190.0120.0220.0200.0000.0200.1790.099
Notifier0.1200.0930.1550.2101.0000.1270.0000.0090.0000.0000.0000.0000.0310.0000.0150.3070.222
PH_considered_TP_at_fault0.2140.0190.0430.5100.1271.0000.0000.0640.0000.0000.0000.0300.0370.0000.0000.0930.042
TP_injury_fatality0.0020.0390.0300.0000.0000.0001.0000.0920.0000.0050.0000.1270.0530.0000.0000.0380.039
TP_injury_traumatic0.0200.1880.0410.0560.0090.0640.0921.0000.2510.0320.0000.1410.2330.1210.0000.1060.000
TP_region_north0.0070.0460.0000.0300.0000.0000.0000.2511.0000.0000.0000.0440.0770.0000.0000.0390.000
TP_type_bike0.0000.0000.0000.0190.0000.0000.0050.0320.0001.0000.0000.0000.0000.0000.0000.0000.000
TP_type_cyclist0.0000.0000.0360.0120.0000.0000.0000.0000.0000.0001.0000.0000.0120.0000.0000.0000.000
TP_type_insd_pass_back0.0270.1430.0220.0220.0000.0300.1270.1410.0440.0000.0001.0000.0390.0000.0000.0610.000
TP_type_pass_front0.0000.0870.0460.0200.0310.0370.0530.2330.0770.0000.0120.0391.0000.0000.0000.0720.004
TP_type_pedestrian0.0000.0000.0000.0000.0000.0000.0000.1210.0000.0000.0000.0000.0001.0000.0000.0000.000
Vechile_registration_present0.0000.0000.0180.0200.0150.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0070.032
Vehicle_mobile0.0700.3750.3460.1790.3070.0930.0380.1060.0390.0000.0000.0610.0720.0000.0071.0000.338
Weather_conditions0.0480.0760.2240.0990.2220.0420.0390.0000.0000.0000.0000.0000.0040.0000.0320.3381.000
2025-07-08T18:22:32.897900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Capped IncurredClaim NumberInception_to_lossIncident_details_presentIncurredInjury_details_presentLocation_of_incidentMain_driverNotification_periodNotifierPH_considered_TP_at_faultTP_injury_fatalityTP_injury_nkTP_injury_traumaticTP_injury_unclearTP_injury_whiplashTP_region_eastangTP_region_eastmidTP_region_londonTP_region_northTP_region_northwTP_region_outerldnTP_region_scotlandTP_region_southeTP_region_southwTP_region_walesTP_region_westmidTP_region_yorkshireTP_type_bikeTP_type_cyclistTP_type_driverTP_type_insd_pass_backTP_type_nkTP_type_otherTP_type_pass_backTP_type_pass_frontTP_type_pedestrianTime_hourVechile_registration_presentVehicle_mobileWeather_conditions
Capped Incurred1.0000.003-0.0140.0261.0000.1650.0490.016-0.0070.0460.0380.111-0.1410.190-0.2790.4910.0540.0350.0350.0600.1060.0540.0740.0680.0440.0900.0860.1030.0980.0000.2260.072-0.2240.2180.2240.2900.0510.0100.0000.1640.021
Claim Number0.0031.0000.0290.3000.0030.0650.0550.545-0.0580.4030.5550.0270.1700.056-0.0840.055-0.033-0.027-0.0110.009-0.014-0.015-0.006-0.087-0.096-0.029-0.002-0.0380.0000.0000.6460.040-0.654-0.0620.0380.0440.000-0.0030.0150.1220.242
Inception_to_loss-0.0140.0291.0000.020-0.0140.0000.0000.000-0.0110.0000.0000.0000.0010.024-0.001-0.0120.0230.0000.0020.000-0.012-0.010-0.010-0.0110.008-0.0140.017-0.0160.0000.0000.0140.000-0.014-0.004-0.0010.0000.007-0.0080.0000.0000.001
Incident_details_present0.0260.3000.0201.0000.0000.0970.0500.1680.0440.1200.2140.0020.0470.0200.0140.0290.0000.0190.0030.0070.0190.0000.0000.0280.0000.0000.0140.0040.0000.0000.2470.0270.2430.0160.0000.0000.0000.0390.0000.0700.048
Incurred1.0000.003-0.0140.0001.0000.0750.0660.000-0.0070.0140.0000.270-0.1410.207-0.2790.4900.0540.0350.0350.1040.1060.0540.0730.0680.0450.0900.0860.1030.0550.0000.2260.082-0.2240.2180.2240.0990.0000.0100.0000.0520.029
Injury_details_present0.1650.0650.0000.0970.0751.0000.1710.0260.0510.0930.0190.0390.1430.1880.1490.1340.0500.0590.0000.0460.0430.0370.0620.0360.0350.0310.0220.0560.0000.0000.0870.1430.0610.0610.0280.0870.0000.1360.0000.3750.076
Location_of_incident0.0490.0550.0000.0500.0660.1711.0000.1250.0920.1550.0430.0300.0290.0410.0500.0310.0000.0270.0370.0000.0420.0100.0000.0750.0370.0370.0000.0130.0000.0360.0430.0220.0280.0390.0120.0460.0000.1520.0180.3460.224
Main_driver0.0160.5450.0000.1680.0000.0260.1251.0000.0570.2100.5100.0000.1040.0560.0500.0260.0220.0000.0120.0300.0000.0110.0000.0470.0630.0140.0350.0210.0190.0120.3960.0220.3760.0570.0000.0200.0000.1200.0200.1790.099
Notification_period-0.007-0.058-0.0110.044-0.0070.0510.0920.0571.0000.1000.0120.0000.0320.000-0.0270.042-0.0090.0100.0140.0000.007-0.0120.015-0.029-0.0250.043-0.015-0.0130.0340.038-0.0520.0000.0300.0330.0470.0000.0000.1840.0000.2180.115
Notifier0.0460.4030.0000.1200.0140.0930.1550.2100.1001.0000.1270.0000.0380.0090.0410.0310.0080.0410.0000.0000.0210.0000.0000.0380.0170.0340.0330.0200.0000.0000.0900.0000.0760.0300.0420.0310.0000.1420.0150.3070.222
PH_considered_TP_at_fault0.0380.5550.0000.2140.0000.0190.0430.5100.0120.1271.0000.0000.0850.0640.0540.0470.0000.0000.0000.0000.0000.0000.0000.0390.0480.0010.0000.0170.0000.0000.4030.0300.3840.0460.0000.0370.0000.0000.0000.0930.042
TP_injury_fatality0.1110.0270.0000.0020.2700.0390.0300.0000.0000.0000.0001.0000.0690.0920.0280.0130.5210.0000.0000.0000.0980.0000.0000.2210.0350.0000.0280.0210.0050.0000.0320.1270.0010.0840.0000.0530.0000.0180.0000.0380.039
TP_injury_nk-0.1410.1700.0010.047-0.1410.1430.0290.1040.0320.0380.0850.0691.0000.0880.381-0.133-0.130-0.139-0.0940.046-0.148-0.126-0.083-0.250-0.236-0.140-0.165-0.1760.0100.0000.1470.218-0.0330.079-0.0200.1100.000-0.0100.0480.0940.023
TP_injury_traumatic0.1900.0560.0240.0200.2070.1880.0410.0560.0000.0090.0640.0920.0881.0000.1570.0980.0750.1180.0220.2510.1880.0460.0640.2870.0580.0720.0660.1020.0320.0000.1050.1410.0730.0670.1270.2330.1210.0580.0000.1060.000
TP_injury_unclear-0.279-0.084-0.0010.014-0.2790.1490.0500.050-0.0270.0410.0540.0280.3810.1571.000-0.4830.008-0.007-0.0160.038-0.0660.007-0.001-0.017-0.007-0.046-0.027-0.0220.0490.017-0.0730.2450.2850.200-0.0800.1850.034-0.0090.2900.1480.036
TP_injury_whiplash0.4910.055-0.0120.0290.4900.1340.0310.0260.0420.0310.0470.013-0.1330.098-0.4831.0000.0340.0790.0590.1090.1550.0620.0660.0860.0550.1100.1180.1110.0520.0210.3070.149-0.2960.1990.3600.4470.0000.0510.0770.1290.020
TP_region_eastang0.054-0.0330.0230.0000.0540.0500.0000.022-0.0090.0080.0000.521-0.1300.0750.0080.0341.000-0.029-0.0120.000-0.041-0.023-0.023-0.048-0.053-0.039-0.041-0.0390.0000.0000.0100.0900.0030.0170.0310.1160.000-0.0020.0000.0390.024
TP_region_eastmid0.035-0.0270.0000.0190.0350.0590.0270.0000.0100.0410.0000.000-0.1390.118-0.0070.079-0.0291.000-0.0210.000-0.030-0.032-0.026-0.061-0.059-0.040-0.024-0.0320.0000.1470.0250.041-0.0050.0580.0370.1370.0000.0180.0000.0360.000
TP_region_london0.035-0.0110.0020.0030.0350.0000.0370.0120.0140.0000.0000.000-0.0940.022-0.0160.059-0.012-0.0211.0000.000-0.0240.008-0.008-0.013-0.037-0.022-0.031-0.0240.0000.0000.0030.2070.0020.0140.0700.0630.0000.0090.0000.0530.015
TP_region_north0.0600.0090.0000.0070.1040.0460.0000.0300.0000.0000.0000.0000.0460.2510.0380.1090.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0440.0000.0000.0930.0770.0000.0460.0000.0390.000
TP_region_northw0.106-0.014-0.0120.0190.1060.0430.0420.0000.0070.0210.0000.098-0.1480.188-0.0660.155-0.041-0.030-0.0240.0001.000-0.036-0.028-0.066-0.063-0.042-0.049-0.0410.0550.0000.0600.139-0.0370.0490.0860.2180.0000.0220.0000.0180.000
TP_region_outerldn0.054-0.015-0.0100.0000.0540.0370.0100.011-0.0120.0000.0000.000-0.1260.0460.0070.062-0.023-0.0320.0080.000-0.0361.000-0.022-0.034-0.040-0.038-0.033-0.0380.0000.0000.0170.0000.0100.0200.0420.1310.000-0.0000.0000.0000.000
TP_region_scotland0.074-0.006-0.0100.0000.0730.0620.0000.0000.0150.0000.0000.000-0.0830.064-0.0010.066-0.023-0.026-0.0080.000-0.028-0.0221.000-0.037-0.036-0.021-0.025-0.0310.0000.0000.0080.054-0.0060.0480.0100.0880.0000.0030.0000.0320.033
TP_region_southe0.068-0.087-0.0110.0280.0680.0360.0750.047-0.0290.0380.0390.221-0.2500.287-0.0170.086-0.048-0.061-0.0130.000-0.066-0.034-0.0371.000-0.089-0.060-0.074-0.0770.0000.000-0.0250.1930.0390.0560.0390.1530.000-0.0010.0350.0620.020
TP_region_southw0.044-0.0960.0080.0000.0450.0350.0370.063-0.0250.0170.0480.035-0.2360.058-0.0070.055-0.053-0.059-0.0370.000-0.063-0.040-0.036-0.0891.000-0.060-0.062-0.0610.0000.000-0.0520.0180.0600.0170.0100.1270.0000.0090.0000.0310.032
TP_region_wales0.090-0.029-0.0140.0000.0900.0310.0370.0140.0430.0340.0010.000-0.1400.072-0.0460.110-0.039-0.040-0.0220.000-0.042-0.038-0.021-0.060-0.0601.000-0.049-0.0460.0000.0000.0260.093-0.0200.0510.0410.1990.0000.0070.4080.0600.023
TP_region_westmid0.086-0.0020.0170.0140.0860.0220.0000.035-0.0150.0330.0000.028-0.1650.066-0.0270.118-0.041-0.024-0.0310.000-0.049-0.033-0.025-0.074-0.062-0.0491.000-0.0540.0620.0000.0520.029-0.0190.0290.0560.1690.2160.0060.0000.0300.000
TP_region_yorkshire0.103-0.038-0.0160.0040.1030.0560.0130.021-0.0130.0200.0170.021-0.1760.102-0.0220.111-0.039-0.032-0.0240.000-0.041-0.038-0.031-0.077-0.061-0.046-0.0541.0000.0000.0000.0160.096-0.0020.0720.0700.1640.0000.0130.0000.0360.017
TP_type_bike0.0980.0000.0000.0000.0550.0000.0000.0190.0340.0000.0000.0050.0100.0320.0490.0520.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0620.0001.0000.0000.0620.0000.0330.0000.0000.0000.0000.0000.0000.0000.000
TP_type_cyclist0.0000.0000.0000.0000.0000.0000.0360.0120.0380.0000.0000.0000.0000.0000.0170.0210.0000.1470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.000
TP_type_driver0.2260.6460.0140.2470.2260.0870.0430.396-0.0520.0900.4030.0320.1470.105-0.0730.3070.0100.0250.0030.0240.0600.0170.008-0.025-0.0520.0260.0520.0160.0620.0001.0000.025-0.823-0.0110.1110.1190.0710.0170.0840.0860.033
TP_type_insd_pass_back0.0720.0400.0000.0270.0820.1430.0220.0220.0000.0000.0300.1270.2180.1410.2450.1490.0900.0410.2070.0440.1390.0000.0540.1930.0180.0930.0290.0960.0000.0000.0251.0000.0310.0000.0310.0390.0000.0310.0000.0610.000
TP_type_nk-0.224-0.654-0.0140.243-0.2240.0610.0280.3760.0300.0760.3840.001-0.0330.0730.285-0.2960.003-0.0050.0020.000-0.0370.010-0.0060.0390.060-0.020-0.019-0.0020.0330.000-0.8230.0311.000-0.164-0.1070.0940.000-0.0200.0000.0690.046
TP_type_other0.218-0.062-0.0040.0160.2180.0610.0390.0570.0330.0300.0460.0840.0790.0670.2000.1990.0170.0580.0140.0000.0490.0200.0480.0560.0170.0510.0290.0720.0000.000-0.0110.000-0.1641.0000.0810.0690.000-0.0100.1640.0790.024
TP_type_pass_back0.2240.038-0.0010.0000.2240.0280.0120.0000.0470.0420.0000.000-0.0200.127-0.0800.3600.0310.0370.0700.0930.0860.0420.0100.0390.0100.0410.0560.0700.0000.0000.1110.031-0.1070.0811.0000.2540.0000.0160.0060.0730.025
TP_type_pass_front0.2900.0440.0000.0000.0990.0870.0460.0200.0000.0310.0370.0530.1100.2330.1850.4470.1160.1370.0630.0770.2180.1310.0880.1530.1270.1990.1690.1640.0000.0120.1190.0390.0940.0690.2541.0000.0000.0520.0000.0720.004
TP_type_pedestrian0.0510.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1210.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2160.0000.0000.0000.0710.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
Time_hour0.010-0.003-0.0080.0390.0100.1360.1520.1200.1840.1420.0000.018-0.0100.058-0.0090.051-0.0020.0180.0090.0460.022-0.0000.003-0.0010.0090.0070.0060.0130.0000.0000.0170.031-0.020-0.0100.0160.0520.0001.0000.0000.3740.253
Vechile_registration_present0.0000.0150.0000.0000.0000.0000.0180.0200.0000.0150.0000.0000.0480.0000.2900.0770.0000.0000.0000.0000.0000.0000.0000.0350.0000.4080.0000.0000.0000.0000.0840.0000.0000.1640.0060.0000.0000.0001.0000.0070.032
Vehicle_mobile0.1640.1220.0000.0700.0520.3750.3460.1790.2180.3070.0930.0380.0940.1060.1480.1290.0390.0360.0530.0390.0180.0000.0320.0620.0310.0600.0300.0360.0000.0000.0860.0610.0690.0790.0730.0720.0000.3740.0071.0000.338
Weather_conditions0.0210.2420.0010.0480.0290.0760.2240.0990.1150.2220.0420.0390.0230.0000.0360.0200.0240.0000.0150.0000.0000.0000.0330.0200.0320.0230.0000.0170.0000.0000.0330.0000.0460.0240.0250.0040.0000.2530.0320.3381.000

Missing values

2025-07-08T18:22:28.507487image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-08T18:22:28.699985image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-08T18:22:28.800445image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Claim Numberdate_of_lossNotifierNotification_periodInception_to_lossLocation_of_incidentWeather_conditionsVehicle_mobileTime_hourMain_driverPH_considered_TP_at_faultVechile_registration_presentIncident_details_presentInjury_details_presentTP_type_insd_pass_backTP_type_insd_pass_frontTP_type_driverTP_type_pass_backTP_type_pass_frontTP_type_bikeTP_type_cyclistTP_type_pass_multiTP_type_pedestrianTP_type_otherTP_type_nkTP_injury_whiplashTP_injury_traumaticTP_injury_fatalityTP_injury_unclearTP_injury_nkTP_region_eastangTP_region_eastmidTP_region_londonTP_region_northTP_region_northwTP_region_outerldnTP_region_scotlandTP_region_southeTP_region_southwTP_region_walesTP_region_westmidTP_region_yorkshireIncurredCapped Incurred
012003-04-15PH2213Main RoadNORMALY10Othern/k1000000000000100010000010000000NaNNaN
122003-04-20CNF19Main RoadWETY18Othern/k11000000000001000100000000100002801.02801.0
232003-04-24CNF517Main RoadWETY16Yn/k10000000000001000100000100000001221.01221.0
342003-05-13CNF123Main RoadN/KY14Othern/k11000000000001000100000000000103530.03530.0
452003-06-11CNF148OtherN/KN9Othern/k11000000000001000110000000000003156.03156.0
562003-06-24PH1623OtherN/KN0Othern/k110001000000000011200000000000010502.010502.0
672003-07-16Other54Main RoadNORMALN18Othern/k100000000000010001000010000000091.091.0
782003-07-17Other040Main RoadWETY7Othern/k11000000000001000100000000001009130.09130.0
892003-07-20PH426Minor RoadWETY22Othern/k101000000000010001100000000000081.081.0
9102003-07-29CNF285Main RoadN/KN22Othern/k1010000000000100010000100000000447.0447.0
Claim Numberdate_of_lossNotifierNotification_periodInception_to_lossLocation_of_incidentWeather_conditionsVehicle_mobileTime_hourMain_driverPH_considered_TP_at_faultVechile_registration_presentIncident_details_presentInjury_details_presentTP_type_insd_pass_backTP_type_insd_pass_frontTP_type_driverTP_type_pass_backTP_type_pass_frontTP_type_bikeTP_type_cyclistTP_type_pass_multiTP_type_pedestrianTP_type_otherTP_type_nkTP_injury_whiplashTP_injury_traumaticTP_injury_fatalityTP_injury_unclearTP_injury_nkTP_region_eastangTP_region_eastmidTP_region_londonTP_region_northTP_region_northwTP_region_outerldnTP_region_scotlandTP_region_southeTP_region_southwTP_region_walesTP_region_westmidTP_region_yorkshireIncurredCapped Incurred
768176822015-06-29Other134Main RoadNORMALN4YN1110011100000000023000000000000930.0930.0
768276832015-06-29Other1249Minor RoadN/KN10OtherN1110020100000010110100000000020113759.050000.0
768376842015-06-29Other041Main RoadN/KY16Y#1100010000000000011000000000000NaNNaN
768476852015-06-29Other073Main RoadN/KN16YN11000100000000000110000000000006085.06085.0
768576862015-06-30Other1128Main RoadN/KN18YN101001100000000002000000020000017503.017503.0
768676872015-06-30Other183Main RoadNORMALN16OtherN1110010000000000010000010000000703.0703.0
768776882015-06-30Other025Minor RoadN/KY14YN111001010000002000000000000000242981.042981.0
768876892015-06-30Other060Minor RoadNORMALY9OtherN11000100000000000100000000100005175.05175.0
768976902015-06-30Other1253Minor RoadNORMALN19OtherN111001000000000001100000000000030072.030072.0
769076912015-06-30Other0266Minor RoadNORMALY14YN11000100000000000100000000000011925.01925.0